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GitHub - pnnl/neuromancer: Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.
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d="M12 1C5.9225 1 1 5.9225 1 12C1 16.8675 4.14875 20.9787 8.52125 22.4362C9.07125 22.5325 9.2775 22.2025 9.2775 21.9137C9.2775 21.6525 9.26375 20.7862 9.26375 19.865C6.5 20.3737 5.785 19.1912 5.565 18.5725C5.44125 18.2562 4.905 17.28 4.4375 17.0187C4.0525 16.8125 3.5025 16.3037 4.42375 16.29C5.29 16.2762 5.90875 17.0875 6.115 17.4175C7.105 19.0812 8.68625 18.6137 9.31875 18.325C9.415 17.61 9.70375 17.1287 10.02 16.8537C7.5725 16.5787 5.015 15.63 5.015 11.4225C5.015 10.2262 5.44125 9.23625 6.1425 8.46625C6.0325 8.19125 5.6475 7.06375 6.2525 5.55125C6.2525 5.55125 7.17375 5.2625 9.2775 6.67875C10.1575 6.43125 11.0925 6.3075 12.0275 6.3075C12.9625 6.3075 13.8975 6.43125 14.7775 6.67875C16.8813 5.24875 17.8025 5.55125 17.8025 5.55125C18.4075 7.06375 18.0225 8.19125 17.9125 8.46625C18.6138 9.23625 19.04 10.2125 19.04 11.4225C19.04 15.6437 16.4688 16.5787 14.0213 16.8537C14.42 17.1975 14.7638 17.8575 14.7638 18.8887C14.7638 20.36 14.75 21.5425 14.75 21.9137C14.75 22.2025 14.9563 22.5462 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class="HeaderMenu-wrapper d-flex flex-column flex-self-start flex-lg-row flex-auto rounded rounded-lg-0"> <nav class="HeaderMenu-nav" aria-label="Global"> <ul class="d-lg-flex list-style-none"> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Product <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 d-lg-flex flex-wrap dropdown-menu-wide"> <div class="HeaderMenu-column px-lg-4 border-lg-right mb-4 mb-lg-0 pr-lg-7"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"github_copilot","context":"product","tag":"link","label":"github_copilot_link_product_navbar"}" href="https://github.com/features/copilot"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-copilot color-fg-subtle mr-3"> <path d="M23.922 16.992c-.861 1.495-5.859 5.023-11.922 5.023-6.063 0-11.061-3.528-11.922-5.023A.641.641 0 0 1 0 16.736v-2.869a.841.841 0 0 1 .053-.22c.372-.935 1.347-2.292 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version="1.1" width="24" data-view-component="true" class="octicon octicon-shield-check color-fg-subtle mr-3"> <path d="M16.53 9.78a.75.75 0 0 0-1.06-1.06L11 13.19l-1.97-1.97a.75.75 0 0 0-1.06 1.06l2.5 2.5a.75.75 0 0 0 1.06 0l5-5Z"></path><path d="m12.54.637 8.25 2.675A1.75 1.75 0 0 1 22 4.976V10c0 6.19-3.771 10.704-9.401 12.83a1.704 1.704 0 0 1-1.198 0C5.77 20.705 2 16.19 2 10V4.976c0-.758.489-1.43 1.21-1.664L11.46.637a1.748 1.748 0 0 1 1.08 0Zm-.617 1.426-8.25 2.676a.249.249 0 0 0-.173.237V10c0 5.46 3.28 9.483 8.43 11.426a.199.199 0 0 0 .14 0C17.22 19.483 20.5 15.461 20.5 10V4.976a.25.25 0 0 0-.173-.237l-8.25-2.676a.253.253 0 0 0-.154 0Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Advanced Security</div> Find and fix vulnerabilities </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" 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0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary Link--external" target="_blank" data-analytics-event="{"location":"navbar","action":"github_skills","context":"product","tag":"link","label":"github_skills_link_product_navbar"}" href="https://skills.github.com"> GitHub Skills <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 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1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://pypi.org/project/neuromancer\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a1956a78279edf31d4b8fac1f538705737d09370b6c526f704cc296c9044872e/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f6e6575726f6d616e636572\" alt=\"PyPi Version\" data-canonical-src=\"https://img.shields.io/pypi/v/neuromancer\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/pnnl/neuromancer/blob/master/LICENSE.md\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/3f9f35c866835373f2429f9db357a9b10435ebff479df3e62b494edb3a2fe933/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4253442d626c75652e737667\" alt=\"License\" data-canonical-src=\"https://img.shields.io/badge/license-BSD-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://pnnl.github.io/neuromancer/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/42fff31160be9576e5a5f90c3f95f3c35eda10e01b6c6b4369433f89cf21e69a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63732d6f6e6c696e652d626c75652e737667\" alt=\"Documentation\" data-canonical-src=\"https://img.shields.io/badge/docs-online-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/5d0c4dfe94fc5be22f8500abd17db49e53652ecf79f841f350a3ce2827a67a71/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d4c696768746e696e672d3739326565353f6c6f676f3d7079746f7263686c696768746e696e67266c6f676f436f6c6f723d7768697465\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5d0c4dfe94fc5be22f8500abd17db49e53652ecf79f841f350a3ce2827a67a71/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d4c696768746e696e672d3739326565353f6c6f676f3d7079746f7263686c696768746e696e67266c6f676f436f6c6f723d7768697465\" alt=\"Lightning\" data-canonical-src=\"https://img.shields.io/badge/-Lightning-792ee5?logo=pytorchlightning\u0026amp;logoColor=white\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNeural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations (NeuroMANCER)\u003c/strong\u003e\nis an open-source differentiable programming (DP) library for solving parametric constrained optimization problems,\nphysics-informed system identification, and parametric model-based optimal control.\nNeuroMANCER is written in \u003ca href=\"https://pytorch.org/\" rel=\"nofollow\"\u003ePyTorch\u003c/a\u003e and allows for systematic\nintegration of machine learning with scientific computing for creating end-to-end\ndifferentiable models and algorithms embedded with prior knowledge and physics.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTable of Contents\u003c/h2\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#overview\"\u003eOverview\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#key-features\"\u003eKey Features\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#whats-new-in-v153\"\u003eWhat's New in v1.5.3\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#getting-started\"\u003eGetting Started\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#domain-examples\"\u003eTutorials\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#documentation-and-user-guides\"\u003eDocumentation and User Guides\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eKey Features\u003c/h3\u003e\u003ca id=\"user-content-key-features\" class=\"anchor\" aria-label=\"Permalink: Key Features\" href=\"#key-features\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eLearn To Model, Learn To Control, Learn To Optimize\u003c/strong\u003e: Our library is built to provide end users a multitude of tools to solve Learning To Optimize (L2O), Learning To Model (L2M), and Learning To Control (L2C) tasks. Tackle advanced constrained parametric optimization, model fluid dynamics using physics-informed neural networks, or learn how to control indoor air temperature in buildings to maximize building efficiency.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eSymbolic programming\u003c/strong\u003e interface makes it very easy to define embed prior knowledge of physics, domain knowledge, and constraints into those learning paradigms.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eComprehensive Learning Tools\u003c/strong\u003e: Access a wide array of tutorials and example applications—from basic system identification to advanced predictive control—making it easy for users to learn and apply NeuroMANCER to real-world problems.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eState-of-the-art methods\u003c/strong\u003e: NeuroMANCER is up-to-date with SOTA methods such as Kolgomorov-Arnold Networks (KANs) for function approximation, neural ordinary differential equations (NODEs) and sparse identification of non-linear dynamics (SINDy) for learning to model dynamical systems, and differentiable convex optimization layers for safety constraints in learning to optimize and learning to control.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWhat's New in v1.5.3\u003c/h2\u003e\u003ca id=\"user-content-whats-new-in-v153\" class=\"anchor\" aria-label=\"Permalink: What's New in v1.5.3\" href=\"#whats-new-in-v153\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eThe NeuroMANCER-GPT Assistant\u003c/h3\u003e\u003ca id=\"user-content-the-neuromancer-gpt-assistant\" class=\"anchor\" aria-label=\"Permalink: The NeuroMANCER-GPT Assistant\" href=\"#the-neuromancer-gpt-assistant\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe provide the easy-to-use scripts to convert the contents of the NeuroMANCER library in a way that is suitable for ingestion in RAG-based \"LLM-assistant\" pipelines. Please see \u003ca href=\"https://github.com/pnnl/neuromancer/blob/develop/assistant/README.md\"\u003eAssistant\u003c/a\u003e to read more about how one can quickly spin-up an LLM model to help understand and code in NeuroMANCER.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBuilding Control Comparison: Reinforcement Learning vs Differential Predictive Control Example\u003c/h3\u003e\u003ca id=\"user-content-building-control-comparison-reinforcement-learning-vs-differential-predictive-control-example\" class=\"anchor\" aria-label=\"Permalink: Building Control Comparison: Reinforcement Learning vs Differential Predictive Control Example\" href=\"#building-control-comparison-reinforcement-learning-vs-differential-predictive-control-example\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe add to our growing library of building modeling and control tutorials a highly education and in-depth walkthrough of reinforcement learning (RL) methods and compare them to differential predictive control (DPC) in NeuroMANCER. This notebook shows that the while the RL solution is more adaptive, the DPC solution in NeuroMANCER is more energy-efficien, has smoother control actions, trains faster and is easier constraint enforcement.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePython 3.11 Version Support\u003c/h3\u003e\u003ca id=\"user-content-python-311-version-support\" class=\"anchor\" aria-label=\"Permalink: Python 3.11 Version Support\" href=\"#python-311-version-support\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eNeuroMANCER has been updated to support Python 3.11.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eNew Feature: Improved Node Class\u003c/h3\u003e\u003ca id=\"user-content-new-feature-improved-node-class\" class=\"anchor\" aria-label=\"Permalink: New Feature: Improved Node Class\" href=\"#new-feature-improved-node-class\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe Node class has been updated to accept instantiated Variable objects as well. The change is backwards-compatible and simplifies house-keeping for the user.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNew Colab Examples:\u003c/strong\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e⭐ \u003ca href=\"#energy-systems\"\u003eBuilding Energy Systems Modeling and Control\u003c/a\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eSimply run\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"pip install neuromancer\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003epip install neuromancer\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFor manual installation, please refer to \u003ca href=\"/pnnl/neuromancer/blob/master/INSTALLATION.md\"\u003eInstallation Instructions\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAn extensive set of tutorials can be found in the\n\u003ca href=\"https://github.com/pnnl/neuromancer/tree/master/examples\"\u003eexamples\u003c/a\u003e folder and the \u003ca href=\"#domain-examples\"\u003eTutorials\u003c/a\u003e below.\nInteractive notebook versions of examples are available on Google Colab!\nTest out NeuroMANCER functionality before cloning the repository and setting up an\nenvironment.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe notebooks below introduce the core abstractions of the NeuroMANCER library, in particular, our symbolic programming interface and Node classes.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSymbolic Variables, Nodes, Constraints, Objectives, and Systems Classes\u003c/h3\u003e\u003ca id=\"user-content-symbolic-variables-nodes-constraints-objectives-and-systems-classes\" class=\"anchor\" aria-label=\"Permalink: Symbolic Variables, Nodes, Constraints, Objectives, and Systems Classes\" href=\"#symbolic-variables-nodes-constraints-objectives-and-systems-classes\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/tutorials/part_1_linear_regression.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\nPart 1: Linear regression in PyTorch vs NeuroMANCER.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/tutorials/part_2_variable.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\nPart 2: NeuroMANCER syntax tutorial: variables, constraints, and objectives.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/tutorials/part_3_node.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\nPart 3: NeuroMANCER syntax tutorial: modules, Node, and System class.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eExample\u003c/h3\u003e\u003ca id=\"user-content-example\" class=\"anchor\" aria-label=\"Permalink: Example\" href=\"#example\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eQuick example for how to solve parametric constrained optimization problem using NeuroMANCER, leveraging our symbolic programming interface, Node and Variable, Blocks, SLiM library, and PenaltyLoss classes.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Neuromancer syntax example for constrained optimization\nimport neuromancer as nm\nimport torch \n\n# define neural architecture \nfunc = nm.modules.blocks.MLP(insize=1, outsize=2, \n linear_map=nm.slim.maps['linear'], \n nonlin=torch.nn.ReLU, hsizes=[80] * 4)\n# wrap neural net into symbolic representation via the Node class: map(p) -\u0026gt; x\nmap = nm.system.Node(func, ['p'], ['x'], name='map')\n \n# define decision variables\nx = nm.constraint.variable(\u0026quot;x\u0026quot;)[:, [0]]\ny = nm.constraint.variable(\u0026quot;x\u0026quot;)[:, [1]]\n# problem parameters sampled in the dataset\np = nm.constraint.variable('p')\n\n# define objective function\nf = (1-x)**2 + (y-x**2)**2\nobj = f.minimize(weight=1.0)\n\n# define constraints\ncon_1 = 100.*(x \u0026gt;= y)\ncon_2 = 100.*(x**2+y**2 \u0026lt;= p**2)\n\n# create penalty method-based loss function\nloss = nm.loss.PenaltyLoss(objectives=[obj], constraints=[con_1, con_2])\n# construct differentiable constrained optimization problem\nproblem = nm.problem.Problem(nodes=[map], loss=loss)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e# Neuromancer syntax example for constrained optimization\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eneuromancer\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enm\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e \n\n\u003cspan class=\"pl-c\"\u003e# define neural architecture \u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003efunc\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enm\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003emodules\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eblocks\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eMLP\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003einsize\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eoutsize\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e, \n \u003cspan class=\"pl-s1\"\u003elinear_map\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003enm\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eslim\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003emaps\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'linear'\u003c/span\u003e], \n \u003cspan class=\"pl-s1\"\u003enonlin\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003enn\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eReLU\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ehsizes\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e80\u003c/span\u003e] \u003cspan class=\"pl-c1\"\u003e*\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e4\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e# wrap neural net into symbolic representation via the Node class: map(p) -\u0026gt; x\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003emap\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enm\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003esystem\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eNode\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003efunc\u003c/span\u003e, [\u003cspan class=\"pl-s\"\u003e'p'\u003c/span\u003e], [\u003cspan class=\"pl-s\"\u003e'x'\u003c/span\u003e], \u003cspan class=\"pl-s1\"\u003ename\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'map'\u003c/span\u003e)\n \n\u003cspan class=\"pl-c\"\u003e# define decision variables\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ex\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enm\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003econstraint\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003evariable\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"x\"\u003c/span\u003e)[:, [\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e]]\n\u003cspan class=\"pl-s1\"\u003ey\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enm\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003econstraint\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003evariable\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"x\"\u003c/span\u003e)[:, [\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e]]\n\u003cspan class=\"pl-c\"\u003e# problem parameters sampled in the dataset\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ep\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enm\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003econstraint\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003evariable\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'p'\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# define objective function\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e (\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ex\u003c/span\u003e)\u003cspan class=\"pl-c1\"\u003e**\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e+\u003c/span\u003e (\u003cspan class=\"pl-s1\"\u003ey\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ex\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e**\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e)\u003cspan class=\"pl-c1\"\u003e**\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eobj\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eminimize\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eweight\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1.0\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# define constraints\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003econ_1\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e100.\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e*\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ex\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u0026gt;=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ey\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003econ_2\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e100.\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e*\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ex\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e**\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e+\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ey\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e**\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u0026lt;=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ep\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e**\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# create penalty method-based loss function\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eloss\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enm\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eloss\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ePenaltyLoss\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eobjectives\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-s1\"\u003eobj\u003c/span\u003e], \u003cspan class=\"pl-s1\"\u003econstraints\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-s1\"\u003econ_1\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003econ_2\u003c/span\u003e])\n\u003cspan class=\"pl-c\"\u003e# construct differentiable constrained optimization problem\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eproblem\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enm\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eproblem\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eProblem\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003enodes\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-s1\"\u003emap\u003c/span\u003e], \u003cspan class=\"pl-s1\"\u003eloss\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eloss\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDomain Examples\u003c/h2\u003e\u003ca id=\"user-content-domain-examples\" class=\"anchor\" aria-label=\"Permalink: Domain Examples\" href=\"#domain-examples\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eNeuroMANCER is built to tackle a variety of domain-specific modeling and control problems using its array of methods. Here we show how to model and control building energy systems, as well as apply load forecasting techniques.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFor more in-depth coverage of our methods, please see our general \u003ca href=\"#tutorials-on-methods-for-modeling-optimization-and-control\"\u003eTutorials\u003c/a\u003e section below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eEnergy Systems\u003c/h3\u003e\u003ca id=\"user-content-energy-systems\" class=\"anchor\" aria-label=\"Permalink: Energy Systems\" href=\"#energy-systems\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/domain_examples/NODE_building_dynamics.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Learning Building Thermal Dynamics using Neural ODEs \n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/domain_examples/NODE_RC_networks.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Multi-zone Building Thermal Dynamics Resistance-Capacitance network with Neural ODEs\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/domain_examples/NODE_swing_equation.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Learning Swing Equation Dynamics using Neural ODEs\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/domain_examples/DPC_building_control.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Learning to Control Indoor Air Temperature in Buildings\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/domain_examples/HVAC_load_forecasting.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Energy Load Forecasting for Building with MLP and CNN Models\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/domain_examples/building_load_forecasting_Transformers.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Energy Load Forecasting for Building with Transformers Model\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/domain_examples/DPC_PSH.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Learning to Control Pumped-storage Hyrdoelectricity System\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://github.com/pnnl/neuromancer/blob/master/examples/domain_examples/RL_DPC_building_control.ipynb\"\u003e\n\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Learning to Control a Building With Safe Reinforcement Learning and Differentiable Predictive Control\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTutorials on Methods for Modeling, Optimization, and Control\u003c/h2\u003e\u003ca id=\"user-content-tutorials-on-methods-for-modeling-optimization-and-control\" class=\"anchor\" aria-label=\"Permalink: Tutorials on Methods for Modeling, Optimization, and Control\" href=\"#tutorials-on-methods-for-modeling-optimization-and-control\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLearning to Optimize (L2O) Parametric Programming\u003c/h3\u003e\u003ca id=\"user-content-learning-to-optimize-l2o-parametric-programming\" class=\"anchor\" aria-label=\"Permalink: Learning to Optimize (L2O) Parametric Programming\" href=\"#learning-to-optimize-l2o-parametric-programming\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eNeuromancer allows you to formulate and solve a broad class of parametric optimization problems, leveraging machine learning to learn the solutions to such problems. \u003ca href=\"https://github.com/pnnl/neuromancer/tree/develop/examples/parametric_programming\"\u003eMore information on Parametric programming\u003c/a\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/parametric_programming/Part_1_basics.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\nPart 1: Learning to solve a constrained optimization problem.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/parametric_programming/Part_2_pQP.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\nPart 2: Learning to solve a quadratically-constrained optimization problem.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/parametric_programming/Part_3_pNLP.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\nPart 3: Learning to solve a set of 2D constrained optimization problems.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/parametric_programming/Part_4_projectedGradient.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\nPart 4: Learning to solve a constrained optimization problem with the projected gradient.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/parametric_programming/Part_5_cvxpy_layers.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\nPart 5: Using Cvxpylayers for differentiable projection onto the polytopic feasible set.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/parametric_programming/Part_6_pQp_lopoCorrection.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\nPart 6: Learning to optimize with metric learning for Operator Splitting layers.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLearning to Control (L2C)\u003c/h3\u003e\u003ca id=\"user-content-learning-to-control-l2c\" class=\"anchor\" aria-label=\"Permalink: Learning to Control (L2C)\" href=\"#learning-to-control-l2c\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eNeuromancer allows you to learn control policies for a full spectrum of white/grey/black-box dynamical systems, subject to choice constraints and objective functions.\n\u003ca href=\"https://github.com/pnnl/neuromancer/tree/develop/examples/control\"\u003eMore information on Differential Predictive Control\u003c/a\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/control/Part_1_stabilize_linear_system.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Part 1: Learning to stabilize a linear dynamical system.\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/control/Part_2_stabilize_ODE.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Part 2: Learning to stabilize a nonlinear differential equation.\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/control/Part_3_ref_tracking_ODE.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Part 3: Learning to control a nonlinear differential equation.\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/control/Part_4_NODE_control.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Part 4: Learning neural ODE model and control policy for an unknown dynamical system.\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/control/Part_5_neural_Lyapunov.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Part 5: Learning neural Lyapunov function for a nonlinear dynamical system.\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFunction Approximation\u003c/h3\u003e\u003ca id=\"user-content-function-approximation\" class=\"anchor\" aria-label=\"Permalink: Function Approximation\" href=\"#function-approximation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eNeuromancer is up-to-date with state-of-the-art methods. Here we showcase the powerful Kolgomorov-Arnold networks \u003ca href=\"https://github.com/pnnl/neuromancer/tree/develop/examples/KANs\"\u003eMore information on Kolgomorov-Arnold Networks\u003c/a\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/feature/fbkans/examples/KANs/p1_fbkan_vs_kan_noise_data_1d.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Part 1: A comparison of KANs and FBKANs in learning a 1D multiscale function with noise\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/feature/fbkans/examples/KANs/p2_fbkan_vs_kan_noise_data_2d.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Part 2: A comparison of KANs and FBKANs in learning a 2D multiscale function with noise\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSystem Identification\u003c/h3\u003e\u003ca id=\"user-content-system-identification\" class=\"anchor\" aria-label=\"Permalink: System Identification\" href=\"#system-identification\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eNeuromancer allows one to use machine learning, prior physics, and domain knowledge to construct data-driven models of dynamical systems given the measured observations of the system behavior.\n\u003ca href=\"https://github.com/pnnl/neuromancer/tree/develop/examples/ODEs\"\u003eMore information on System ID via Neural State Space Models and ODEs\u003c/a\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/ODEs/Part_1_NODE.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Part 1: Neural Ordinary Differential Equations (NODEs)\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/ODEs/Part_2_param_estim_ODE.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Part 2: Parameter estimation of ODE system\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/ODEs/Part_3_UDE.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Part 3: Universal Differential Equations (UDEs)\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/ODEs/Part_4_nonauto_NODE.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Part 4: NODEs with exogenous inputs\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/ODEs/Part_5_nonauto_NSSM.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Part 5: Neural State Space Models (NSSMs) with exogenous inputs\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/ODEs/Part_6_NetworkODE.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Part 6: Data-driven modeling of resistance-capacitance (RC) network ODEs\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/ODEs/Part_7_DeepKoopman.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Part 7: Deep Koopman operator\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/ODEs/Part_8_nonauto_DeepKoopman.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Part 8: control-oriented Deep Koopman operator\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/ODEs/Part_9_SINDy.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Part 9: Sparse Identification of Nonlinear Dynamics (SINDy)\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePhysics-Informed Neural Networks (PINNs)\u003c/h3\u003e\u003ca id=\"user-content-physics-informed-neural-networks-pinns\" class=\"anchor\" aria-label=\"Permalink: Physics-Informed Neural Networks (PINNs)\" href=\"#physics-informed-neural-networks-pinns\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eNeuromancer's symbolic programming design is perfectly suited for solving PINNs. \u003ca href=\"https://github.com/pnnl/neuromancer/tree/develop/examples/PDEs\"\u003eMore information on PINNs\u003c/a\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/PDEs/Part_1_PINN_DiffusionEquation.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Part 1: Diffusion Equation\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/PDEs/Part_2_PINN_BurgersEquation.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Part 2: Burgers' Equation\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/PDEs/Part_3_PINN_BurgersEquation_inverse.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Part 3: Burgers' Equation w/ Parameter Estimation (Inverse Problem)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/PDEs/Part_4_PINN_LaplaceEquationSteadyState.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Part 4: Laplace's Equation (steady-state)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/PDEs/Part_5_Pendulum_Stacked.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Part 5: Damped Pendulum (stacked PINN)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/PDEs/Part_6_PINN_NavierStokesCavitySteady_KAN.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Part 6: Navier-Stokes equation (lid-driven cavity flow, steady-state, KAN)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eStochastic Differential Equations (SDEs)\u003c/h3\u003e\u003ca id=\"user-content-stochastic-differential-equations-sdes\" class=\"anchor\" aria-label=\"Permalink: Stochastic Differential Equations (SDEs)\" href=\"#stochastic-differential-equations-sdes\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eNeuromancer has been integrated with TorchSDE to handle stochastic dynamical systems. \u003ca href=\"https://github.com/pnnl/neuromancer/tree/develop/examples/SDEs\"\u003eMore information on SDEs\u003c/a\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/SDEs/sde_walkthrough.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e LatentSDEs: \"System Identification\" of Stochastic Processes using Neuromancer x TorchSDE\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eScalability and Customization\u003c/h2\u003e\u003ca id=\"user-content-scalability-and-customization\" class=\"anchor\" aria-label=\"Permalink: Scalability and Customization\" href=\"#scalability-and-customization\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePyTorch Lightning Integration\u003c/h3\u003e\u003ca id=\"user-content-pytorch-lightning-integration\" class=\"anchor\" aria-label=\"Permalink: PyTorch Lightning Integration\" href=\"#pytorch-lightning-integration\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe have integrated PyTorch Lightning to streamline code, enable custom training logic, support GPU and multi-GPU setups, and handle large-scale, memory-intensive learning tasks.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/lightning_integration_examples/Part_1_lightning_basics_tutorial.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Part 1: Lightning Integration Basics.\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/lightning_integration_examples/Part_2_lightning_advanced_and_gpu_tutorial.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Part 2: Lightning Advanced Features and Automatic GPU Support.\n\u003c/li\u003e\n\u003cli\u003e\n\u003ca href=\"https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/lightning_integration_examples/other_examples/lightning_custom_training_example.ipynb\" rel=\"nofollow\"\u003e\n\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e Part 3: Defining Custom Training Logic via Lightning Modularized Code.\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDocumentation and User Guides\u003c/h2\u003e\u003ca id=\"user-content-documentation-and-user-guides\" class=\"anchor\" aria-label=\"Permalink: Documentation and User Guides\" href=\"#documentation-and-user-guides\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe documentation for the library can be found \u003ca href=\"https://pnnl.github.io/neuromancer/\" rel=\"nofollow\"\u003eonline\u003c/a\u003e.\nThere is also an \u003ca href=\"https://www.youtube.com/watch?v=YkFKz-DgC98\" rel=\"nofollow\"\u003eintroduction video\u003c/a\u003e covering\nthe core features of the library.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFor more information, including that for developers, please go to our \u003ca href=\"/pnnl/neuromancer/blob/master/USER_GUIDE.md\"\u003eDeveloper and User Guide\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCommunity Information\u003c/h2\u003e\u003ca id=\"user-content-community-information\" class=\"anchor\" aria-label=\"Permalink: Community Information\" href=\"#community-information\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe welcome contributions and feedback from the open-source community!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eContributions, Discussions, and Issues\u003c/h3\u003e\u003ca id=\"user-content-contributions-discussions-and-issues\" class=\"anchor\" aria-label=\"Permalink: Contributions, Discussions, and Issues\" href=\"#contributions-discussions-and-issues\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePlease read the \u003ca href=\"https://github.com/pnnl/neuromancer/blob/master/CONTRIBUTING.md\"\u003eCommunity Development Guidelines\u003c/a\u003e\nfor further information on contributions, \u003ca href=\"https://github.com/pnnl/neuromancer/discussions\"\u003ediscussions\u003c/a\u003e, and \u003ca href=\"https://github.com/pnnl/neuromancer/issues\"\u003eIssues\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eRelease notes\u003c/h3\u003e\u003ca id=\"user-content-release-notes\" class=\"anchor\" aria-label=\"Permalink: Release notes\" href=\"#release-notes\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eSee the \u003ca href=\"https://github.com/pnnl/neuromancer/blob/master/RELEASE_NOTES.md\"\u003eRelease notes\u003c/a\u003e documenting new features.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLicense\u003c/h3\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eNeuroMANCER comes with \u003ca href=\"https://en.wikipedia.org/wiki/BSD_licenses\" rel=\"nofollow\"\u003eBSD license\u003c/a\u003e.\nSee the \u003ca href=\"https://github.com/pnnl/neuromancer/blob/master/LICENSE.md\"\u003elicense\u003c/a\u003e for further details.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePublications\u003c/h2\u003e\u003ca id=\"user-content-publications\" class=\"anchor\" aria-label=\"Permalink: Publications\" href=\"#publications\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2503.19333\" rel=\"nofollow\"\u003eAshish S. Nair, Bruno Jacob, Amanda A. Howard, Jan Drgona, Panos Stinis, E-PINNs: Epistemic Physics-Informed Neural Networks, \tarXiv:2503.19333\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2410.11061\" rel=\"nofollow\"\u003eBo Tang, Elias B. Khalil, Ján Drgoňa, Learning to Optimize for Mixed-Integer Non-linear Programming, arXiv:2410.11061, 2024\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2409.13817\" rel=\"nofollow\"\u003eJohn Viljoen, Wenceslao Shaw Cortez, Jan Drgona, Sebastian East, Masayoshi Tomizuka, Draguna Vrabie, Differentiable Predictive Control for Robotics: A Data-Driven Predictive Safety Filter Approach, arXiv:2409.13817, 2024\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/abstract/document/10479163\" rel=\"nofollow\"\u003eJan Drgona, Aaron Tuor, Draguna Vrabie, Learning Constrained Parametric Differentiable Predictive Control Policies With Guarantees, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2024\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2406.10433\" rel=\"nofollow\"\u003eRenukanandan Tumu, Wenceslao Shaw Cortez, Ján Drgoňa, Draguna L. Vrabie, Sonja Glavaski, Differentiable Predictive Control for Large-Scale Urban Road Networks, \tarXiv:2406.10433, 2024\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2404.00882\" rel=\"nofollow\"\u003eEthan King, James Kotary, Ferdinando Fioretto, Jan Drgona, Metric Learning to Accelerate Convergence of Operator Splitting Methods for Differentiable Parametric Programming, arXiv:2404.00882, 2024\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2403.12938\" rel=\"nofollow\"\u003eJames Koch, Madelyn Shapiro, Himanshu Sharma, Draguna Vrabie, Jan Drgona, Neural Differential Algebraic Equations, arXiv:2403.12938, 2024\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2311.08496\" rel=\"nofollow\"\u003eWenceslao Shaw Cortez, Jan Drgona, Draguna Vrabie, Mahantesh Halappanavar, A Robust, Efficient Predictive Safety Filter, arXiv:2311.08496, 2024\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/abstract/document/10253098\" rel=\"nofollow\"\u003eShrirang Abhyankar, Jan Drgona, Andrew August, Elliott Skomski, Aaron Tuor, Neuro-physical dynamic load modeling using differentiable parametric optimization, 2023 IEEE Power \u0026amp; Energy Society General Meeting (PESGM), 2023\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://aps.arxiv.org/abs/2207.04962\" rel=\"nofollow\"\u003eJames Koch, Zhao Chen, Aaron Tuor, Jan Drgona, Draguna Vrabie, Structural Inference of Networked Dynamical Systems with Universal Differential Equations, arXiv:2207.04962, (2022)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.sciencedirect.com/science/article/pii/S2405896322015877\" rel=\"nofollow\"\u003eJán Drgoňa, Sayak Mukherjee, Aaron Tuor, Mahantesh Halappanavar, Draguna Vrabie, Learning Stochastic Parametric Differentiable Predictive Control Policies, IFAC ROCOND conference (2022)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2205.10728\" rel=\"nofollow\"\u003eSayak Mukherjee, Ján Drgoňa, Aaron Tuor, Mahantesh Halappanavar, Draguna Vrabie, Neural Lyapunov Differentiable Predictive Control, IEEE Conference on Decision and Control Conference 2022\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2208.02319\" rel=\"nofollow\"\u003eWenceslao Shaw Cortez, Jan Drgona, Aaron Tuor, Mahantesh Halappanavar, Draguna Vrabie, Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach, IEEE Conference on Decision and Control Conference 2022\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/document/9867379\" rel=\"nofollow\"\u003eEthan King, Jan Drgona, Aaron Tuor, Shrirang Abhyankar, Craig Bakker, Arnab Bhattacharya, Draguna Vrabie, Koopman-based Differentiable Predictive Control for the Dynamics-Aware Economic Dispatch Problem, 2022 American Control Conference (ACC)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.sciencedirect.com/science/article/pii/S0378778821002760\" rel=\"nofollow\"\u003eDrgoňa, J., Tuor, A. R., Chandan, V., \u0026amp; Vrabie, D. L., Physics-constrained deep learning of multi-zone building thermal dynamics. Energy and Buildings, 243, 110992, (2021)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/document/9482930\" rel=\"nofollow\"\u003eE. Skomski, S. Vasisht, C. Wight, A. Tuor, J. Drgoňa and D. Vrabie, \"Constrained Block Nonlinear Neural Dynamical Models,\" 2021 American Control Conference (ACC), 2021, pp. 3993-4000, doi: 10.23919/ACC50511.2021.9482930.\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://proceedings.mlr.press/v144/skomski21a.html\" rel=\"nofollow\"\u003eSkomski, E., Drgoňa, J., \u0026amp; Tuor, A. (2021, May). Automating Discovery of Physics-Informed Neural State Space Models via Learning and Evolution. In Learning for Dynamics and Control (pp. 980-991). PMLR.\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.sciencedirect.com/science/article/pii/S2405896321012933\" rel=\"nofollow\"\u003eDrgoňa, J., Tuor, A., Skomski, E., Vasisht, S., \u0026amp; Vrabie, D. (2021). Deep Learning Explicit Differentiable Predictive Control Laws for Buildings. IFAC-PapersOnLine, 54(6), 14-19.\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2004.10883\" rel=\"nofollow\"\u003eTuor, A., Drgona, J., \u0026amp; Vrabie, D. (2020). Constrained neural ordinary differential equations with stability guarantees. arXiv preprint arXiv:2004.10883.\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.sciencedirect.com/science/article/pii/S0959152422000981\" rel=\"nofollow\"\u003eDrgona, Jan, et al. \"Differentiable Predictive Control: An MPC Alternative for Unknown Nonlinear Systems using Constrained Deep Learning.\" Journal of Process Control Volume 116, August 2022, Pages 80-92\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://ieeexplore.ieee.org/document/9809789\" rel=\"nofollow\"\u003eDrgona, J., Skomski, E., Vasisht, S., Tuor, A., \u0026amp; Vrabie, D. (2020). Dissipative Deep Neural Dynamical Systems, in IEEE Open Journal of Control Systems, vol. 1, pp. 100-112, 2022\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/2004.11184\" rel=\"nofollow\"\u003eDrgona, J., Tuor, A., \u0026amp; Vrabie, D., Learning Constrained Adaptive Differentiable Predictive Control Policies With Guarantees, arXiv preprint arXiv:2004.11184, (2020)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCite as\u003c/h2\u003e\u003ca id=\"user-content-cite-as\" class=\"anchor\" aria-label=\"Permalink: Cite as\" href=\"#cite-as\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-yaml notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"@article{Neuromancer2023,\n title={{NeuroMANCER: Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations}},\n author={Drgona, Jan and Tuor, Aaron and Koch, James and Shapiro, Madelyn and Jacob, Bruno and Vrabie, Draguna},\n Url= {https://github.com/pnnl/neuromancer}, \n year={2023}\n}\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s\"\u003e@article{Neuromancer2023,\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003etitle={{NeuroMANCER\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003eNeural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations}},\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003eauthor={Drgona, Jan and Tuor, Aaron and Koch, James and Shapiro, Madelyn and Jacob, Bruno and Vrabie, Draguna},\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003eUrl= {https://github.com/pnnl/neuromancer}, \u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003eyear={2023}\u003c/span\u003e\n\u003cspan class=\"pl-s\"\u003e}\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDevelopment team\u003c/h2\u003e\u003ca id=\"user-content-development-team\" class=\"anchor\" aria-label=\"Permalink: Development team\" href=\"#development-team\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLead developers\u003c/strong\u003e: \u003ca href=\"https://drgona.github.io/\" rel=\"nofollow\"\u003eJan Drgona\u003c/a\u003e, \u003ca href=\"https://sw.cs.wwu.edu/~tuora/aarontuor/\" rel=\"nofollow\"\u003eAaron Tuor\u003c/a\u003e\n\u003cstrong\u003eActive core developers\u003c/strong\u003e: \u003ca href=\"https://www.linkedin.com/in/rahul-birmiwal009/\" rel=\"nofollow\"\u003eRahul Birmiwal\u003c/a\u003e, \u003ca href=\"https://brunopjacob.github.io/\" rel=\"nofollow\"\u003eBruno Jacob\u003c/a\u003e, \u003ca href=\"https://reillyraab.com/about.html\" rel=\"nofollow\"\u003eReilly Raab\u003c/a\u003e\u003cbr\u003e\n\u003cstrong\u003eNotable contributors\u003c/strong\u003e: Madelyn Shapiro, James Koch, Seth Briney, Bo Tang, Ethan King, Elliot Skomski, Zhao Chen, Christian Møldrup Legaard\u003cbr\u003e\n\u003cstrong\u003eScientific advisors\u003c/strong\u003e: Draguna Vrabie, Panos Stinis\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eOpen-source contributions made by:\u003cbr\u003e\n\u003ca href=\"https://github.com/pnnl/neuromancer/graphs/contributors\"\u003e\n\u003cimg src=\"https://camo.githubusercontent.com/0d284ec35c6d988e07244ebc90b59f0f6b4d6ec9e952b12856140d97b15b9518/68747470733a2f2f636f6e747269622e726f636b732f696d6167653f7265706f3d706e6e6c2f6e6575726f6d616e636572\" data-canonical-src=\"https://contrib.rocks/image?repo=pnnl/neuromancer\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eMade with \u003ca href=\"https://contrib.rocks\" rel=\"nofollow\"\u003econtrib.rocks\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAcknowledgments\u003c/h2\u003e\u003ca id=\"user-content-acknowledgments\" class=\"anchor\" aria-label=\"Permalink: Acknowledgments\" href=\"#acknowledgments\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis research was partially supported by the Mathematics for Artificial Reasoning in Science (MARS) and Data Model Convergence (DMC) initiatives via the Laboratory Directed Research and Development (LDRD) investments at Pacific Northwest National Laboratory (PNNL), by the U.S. Department of Energy, through the Office of Advanced Scientific Computing Research's “Data-Driven Decision Control for Complex Systems (DnC2S)” project, and through the Energy Efficiency and Renewable Energy, Building Technologies Office under the “Dynamic decarbonization through autonomous physics-centric deep learning and optimization of building operations” and the “Advancing Market-Ready Building Energy Management by Cost-Effective Differentiable Predictive Control” projects. This project was also supported from the U.S. Department of Energy, Advanced Scientific Computing Research program, under the Uncertainty Quantification for Multifidelity Operator Learning (MOLUcQ) project (Project No. 81739). PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL0-1830.\u003c/p\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"NeuroMANCER v1.5.3","anchor":"neuromancer-v153","htmlText":"NeuroMANCER v1.5.3"},{"level":2,"text":"Table of Contents","anchor":"table-of-contents","htmlText":"Table of Contents"},{"level":3,"text":"Key Features","anchor":"key-features","htmlText":"Key Features"},{"level":2,"text":"What's New in v1.5.3","anchor":"whats-new-in-v153","htmlText":"What's New in v1.5.3"},{"level":3,"text":"The NeuroMANCER-GPT Assistant","anchor":"the-neuromancer-gpt-assistant","htmlText":"The NeuroMANCER-GPT Assistant"},{"level":3,"text":"Building Control Comparison: Reinforcement Learning vs Differential Predictive Control Example","anchor":"building-control-comparison-reinforcement-learning-vs-differential-predictive-control-example","htmlText":"Building Control Comparison: Reinforcement Learning vs Differential Predictive Control Example"},{"level":3,"text":"Python 3.11 Version Support","anchor":"python-311-version-support","htmlText":"Python 3.11 Version Support"},{"level":3,"text":"New Feature: Improved Node Class","anchor":"new-feature-improved-node-class","htmlText":"New Feature: Improved Node Class"},{"level":2,"text":"Installation","anchor":"installation","htmlText":"Installation"},{"level":2,"text":"Getting Started","anchor":"getting-started","htmlText":"Getting Started"},{"level":3,"text":"Symbolic Variables, Nodes, Constraints, Objectives, and Systems Classes","anchor":"symbolic-variables-nodes-constraints-objectives-and-systems-classes","htmlText":"Symbolic Variables, Nodes, Constraints, Objectives, and Systems Classes"},{"level":3,"text":"Example","anchor":"example","htmlText":"Example"},{"level":2,"text":"Domain Examples","anchor":"domain-examples","htmlText":"Domain Examples"},{"level":3,"text":"Energy Systems","anchor":"energy-systems","htmlText":"Energy Systems"},{"level":2,"text":"Tutorials on Methods for Modeling, Optimization, and Control","anchor":"tutorials-on-methods-for-modeling-optimization-and-control","htmlText":"Tutorials on Methods for Modeling, Optimization, and Control"},{"level":3,"text":"Learning to Optimize (L2O) Parametric Programming","anchor":"learning-to-optimize-l2o-parametric-programming","htmlText":"Learning to Optimize (L2O) Parametric Programming"},{"level":3,"text":"Learning to Control (L2C)","anchor":"learning-to-control-l2c","htmlText":"Learning to Control (L2C)"},{"level":3,"text":"Function Approximation","anchor":"function-approximation","htmlText":"Function Approximation"},{"level":3,"text":"System Identification","anchor":"system-identification","htmlText":"System Identification"},{"level":3,"text":"Physics-Informed Neural Networks (PINNs)","anchor":"physics-informed-neural-networks-pinns","htmlText":"Physics-Informed Neural Networks (PINNs)"},{"level":3,"text":"Stochastic Differential Equations (SDEs)","anchor":"stochastic-differential-equations-sdes","htmlText":"Stochastic Differential Equations (SDEs)"},{"level":2,"text":"Scalability and Customization","anchor":"scalability-and-customization","htmlText":"Scalability and Customization"},{"level":3,"text":"PyTorch Lightning Integration","anchor":"pytorch-lightning-integration","htmlText":"PyTorch Lightning Integration"},{"level":2,"text":"Documentation and User Guides","anchor":"documentation-and-user-guides","htmlText":"Documentation and User Guides"},{"level":2,"text":"Community Information","anchor":"community-information","htmlText":"Community Information"},{"level":3,"text":"Contributions, Discussions, and Issues","anchor":"contributions-discussions-and-issues","htmlText":"Contributions, Discussions, and Issues"},{"level":3,"text":"Release notes","anchor":"release-notes","htmlText":"Release notes"},{"level":3,"text":"License","anchor":"license","htmlText":"License"},{"level":2,"text":"Publications","anchor":"publications","htmlText":"Publications"},{"level":2,"text":"Cite as","anchor":"cite-as","htmlText":"Cite as"},{"level":2,"text":"Development team","anchor":"development-team","htmlText":"Development team"},{"level":2,"text":"Acknowledgments","anchor":"acknowledgments","htmlText":"Acknowledgments"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fpnnl%2Fneuromancer"}},{"displayName":"LICENSE.md","repoName":"neuromancer","refName":"master","path":"LICENSE.md","preferredFileType":"license","tabName":"License","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fpnnl%2Fneuromancer"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-7d7eb7c71814.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-708ec8ade250.js","githubDevUrl":null,"enabled_features":{"copilot_workspace":null,"code_nav_ui_events":false,"overview_shared_code_dropdown_button":true,"react_blob_overlay":false,"accessible_code_button":true,"github_models_repo_integration":false}}}}</script> <div 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version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a href="https://pypi.org/project/neuromancer" rel="nofollow"><img src="https://camo.githubusercontent.com/a1956a78279edf31d4b8fac1f538705737d09370b6c526f704cc296c9044872e/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f6e6575726f6d616e636572" alt="PyPi Version" data-canonical-src="https://img.shields.io/pypi/v/neuromancer" style="max-width: 100%;"></a> <a href="https://github.com/pnnl/neuromancer/blob/master/LICENSE.md"><img src="https://camo.githubusercontent.com/3f9f35c866835373f2429f9db357a9b10435ebff479df3e62b494edb3a2fe933/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4253442d626c75652e737667" alt="License" data-canonical-src="https://img.shields.io/badge/license-BSD-blue.svg" style="max-width: 100%;"></a> <a href="https://pnnl.github.io/neuromancer/" rel="nofollow"><img src="https://camo.githubusercontent.com/42fff31160be9576e5a5f90c3f95f3c35eda10e01b6c6b4369433f89cf21e69a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63732d6f6e6c696e652d626c75652e737667" alt="Documentation" data-canonical-src="https://img.shields.io/badge/docs-online-blue.svg" style="max-width: 100%;"></a> <a target="_blank" rel="noopener noreferrer nofollow" href="https://camo.githubusercontent.com/5d0c4dfe94fc5be22f8500abd17db49e53652ecf79f841f350a3ce2827a67a71/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d4c696768746e696e672d3739326565353f6c6f676f3d7079746f7263686c696768746e696e67266c6f676f436f6c6f723d7768697465"><img src="https://camo.githubusercontent.com/5d0c4dfe94fc5be22f8500abd17db49e53652ecf79f841f350a3ce2827a67a71/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d4c696768746e696e672d3739326565353f6c6f676f3d7079746f7263686c696768746e696e67266c6f676f436f6c6f723d7768697465" alt="Lightning" data-canonical-src="https://img.shields.io/badge/-Lightning-792ee5?logo=pytorchlightning&logoColor=white" style="max-width: 100%;"></a></p> <p dir="auto"><strong>Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations (NeuroMANCER)</strong> is an open-source differentiable programming (DP) library for solving parametric constrained optimization problems, physics-informed system identification, and parametric model-based optimal control. NeuroMANCER is written in <a href="https://pytorch.org/" rel="nofollow">PyTorch</a> and allows for systematic integration of machine learning with scientific computing for creating end-to-end differentiable models and algorithms embedded with prior knowledge and physics.</p> <hr> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Table of Contents</h2><a id="user-content-table-of-contents" class="anchor" aria-label="Permalink: Table of Contents" href="#table-of-contents"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li><a href="#overview">Overview</a></li> <li><a href="#key-features">Key Features</a></li> <li><a href="#whats-new-in-v153">What's New in v1.5.3</a></li> <li><a href="#installation">Installation</a></li> <li><a href="#getting-started">Getting Started</a></li> <li><a href="#domain-examples">Tutorials</a></li> <li><a href="#documentation-and-user-guides">Documentation and User Guides</a></li> </ol> <hr> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Key Features</h3><a id="user-content-key-features" class="anchor" aria-label="Permalink: Key Features" href="#key-features"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><strong>Learn To Model, Learn To Control, Learn To Optimize</strong>: Our library is built to provide end users a multitude of tools to solve Learning To Optimize (L2O), Learning To Model (L2M), and Learning To Control (L2C) tasks. Tackle advanced constrained parametric optimization, model fluid dynamics using physics-informed neural networks, or learn how to control indoor air temperature in buildings to maximize building efficiency.</li> <li><strong>Symbolic programming</strong> interface makes it very easy to define embed prior knowledge of physics, domain knowledge, and constraints into those learning paradigms.</li> <li><strong>Comprehensive Learning Tools</strong>: Access a wide array of tutorials and example applications—from basic system identification to advanced predictive control—making it easy for users to learn and apply NeuroMANCER to real-world problems.</li> <li><strong>State-of-the-art methods</strong>: NeuroMANCER is up-to-date with SOTA methods such as Kolgomorov-Arnold Networks (KANs) for function approximation, neural ordinary differential equations (NODEs) and sparse identification of non-linear dynamics (SINDy) for learning to model dynamical systems, and differentiable convex optimization layers for safety constraints in learning to optimize and learning to control.</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">What's New in v1.5.3</h2><a id="user-content-whats-new-in-v153" class="anchor" aria-label="Permalink: What's New in v1.5.3" href="#whats-new-in-v153"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">The NeuroMANCER-GPT Assistant</h3><a id="user-content-the-neuromancer-gpt-assistant" class="anchor" aria-label="Permalink: The NeuroMANCER-GPT Assistant" href="#the-neuromancer-gpt-assistant"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We provide the easy-to-use scripts to convert the contents of the NeuroMANCER library in a way that is suitable for ingestion in RAG-based "LLM-assistant" pipelines. Please see <a href="https://github.com/pnnl/neuromancer/blob/develop/assistant/README.md">Assistant</a> to read more about how one can quickly spin-up an LLM model to help understand and code in NeuroMANCER.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Building Control Comparison: Reinforcement Learning vs Differential Predictive Control Example</h3><a id="user-content-building-control-comparison-reinforcement-learning-vs-differential-predictive-control-example" class="anchor" aria-label="Permalink: Building Control Comparison: Reinforcement Learning vs Differential Predictive Control Example" href="#building-control-comparison-reinforcement-learning-vs-differential-predictive-control-example"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We add to our growing library of building modeling and control tutorials a highly education and in-depth walkthrough of reinforcement learning (RL) methods and compare them to differential predictive control (DPC) in NeuroMANCER. This notebook shows that the while the RL solution is more adaptive, the DPC solution in NeuroMANCER is more energy-efficien, has smoother control actions, trains faster and is easier constraint enforcement.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Python 3.11 Version Support</h3><a id="user-content-python-311-version-support" class="anchor" aria-label="Permalink: Python 3.11 Version Support" href="#python-311-version-support"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">NeuroMANCER has been updated to support Python 3.11.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">New Feature: Improved Node Class</h3><a id="user-content-new-feature-improved-node-class" class="anchor" aria-label="Permalink: New Feature: Improved Node Class" href="#new-feature-improved-node-class"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The Node class has been updated to accept instantiated Variable objects as well. The change is backwards-compatible and simplifies house-keeping for the user.</p> <p dir="auto"><strong>New Colab Examples:</strong></p> <blockquote> <p dir="auto">⭐ <a href="#energy-systems">Building Energy Systems Modeling and Control</a></p> </blockquote> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Installation</h2><a id="user-content-installation" class="anchor" aria-label="Permalink: Installation" href="#installation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Simply run</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="pip install neuromancer"><pre class="notranslate"><code>pip install neuromancer </code></pre></div> <p dir="auto">For manual installation, please refer to <a href="/pnnl/neuromancer/blob/master/INSTALLATION.md">Installation Instructions</a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Getting Started</h2><a id="user-content-getting-started" class="anchor" aria-label="Permalink: Getting Started" href="#getting-started"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">An extensive set of tutorials can be found in the <a href="https://github.com/pnnl/neuromancer/tree/master/examples">examples</a> folder and the <a href="#domain-examples">Tutorials</a> below. Interactive notebook versions of examples are available on Google Colab! Test out NeuroMANCER functionality before cloning the repository and setting up an environment.</p> <p dir="auto">The notebooks below introduce the core abstractions of the NeuroMANCER library, in particular, our symbolic programming interface and Node classes.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Symbolic Variables, Nodes, Constraints, Objectives, and Systems Classes</h3><a id="user-content-symbolic-variables-nodes-constraints-objectives-and-systems-classes" class="anchor" aria-label="Permalink: Symbolic Variables, Nodes, Constraints, Objectives, and Systems Classes" href="#symbolic-variables-nodes-constraints-objectives-and-systems-classes"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/tutorials/part_1_linear_regression.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 1: Linear regression in PyTorch vs NeuroMANCER.</p> </li> <li> <p dir="auto"><a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/tutorials/part_2_variable.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 2: NeuroMANCER syntax tutorial: variables, constraints, and objectives.</p> </li> <li> <p dir="auto"><a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/tutorials/part_3_node.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 3: NeuroMANCER syntax tutorial: modules, Node, and System class.</p> </li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Example</h3><a id="user-content-example" class="anchor" aria-label="Permalink: Example" href="#example"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Quick example for how to solve parametric constrained optimization problem using NeuroMANCER, leveraging our symbolic programming interface, Node and Variable, Blocks, SLiM library, and PenaltyLoss classes.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Neuromancer syntax example for constrained optimization import neuromancer as nm import torch # define neural architecture func = nm.modules.blocks.MLP(insize=1, outsize=2, linear_map=nm.slim.maps['linear'], nonlin=torch.nn.ReLU, hsizes=[80] * 4) # wrap neural net into symbolic representation via the Node class: map(p) -> x map = nm.system.Node(func, ['p'], ['x'], name='map') # define decision variables x = nm.constraint.variable("x")[:, [0]] y = nm.constraint.variable("x")[:, [1]] # problem parameters sampled in the dataset p = nm.constraint.variable('p') # define objective function f = (1-x)**2 + (y-x**2)**2 obj = f.minimize(weight=1.0) # define constraints con_1 = 100.*(x >= y) con_2 = 100.*(x**2+y**2 <= p**2) # create penalty method-based loss function loss = nm.loss.PenaltyLoss(objectives=[obj], constraints=[con_1, con_2]) # construct differentiable constrained optimization problem problem = nm.problem.Problem(nodes=[map], loss=loss)"><pre><span class="pl-c"># Neuromancer syntax example for constrained optimization</span> <span class="pl-k">import</span> <span class="pl-s1">neuromancer</span> <span class="pl-k">as</span> <span class="pl-s1">nm</span> <span class="pl-k">import</span> <span class="pl-s1">torch</span> <span class="pl-c"># define neural architecture </span> <span class="pl-s1">func</span> <span class="pl-c1">=</span> <span class="pl-s1">nm</span>.<span class="pl-c1">modules</span>.<span class="pl-c1">blocks</span>.<span class="pl-c1">MLP</span>(<span class="pl-s1">insize</span><span class="pl-c1">=</span><span class="pl-c1">1</span>, <span class="pl-s1">outsize</span><span class="pl-c1">=</span><span class="pl-c1">2</span>, <span class="pl-s1">linear_map</span><span class="pl-c1">=</span><span class="pl-s1">nm</span>.<span class="pl-c1">slim</span>.<span class="pl-c1">maps</span>[<span class="pl-s">'linear'</span>], <span class="pl-s1">nonlin</span><span class="pl-c1">=</span><span class="pl-s1">torch</span>.<span class="pl-c1">nn</span>.<span class="pl-c1">ReLU</span>, <span class="pl-s1">hsizes</span><span class="pl-c1">=</span>[<span class="pl-c1">80</span>] <span class="pl-c1">*</span> <span class="pl-c1">4</span>) <span class="pl-c"># wrap neural net into symbolic representation via the Node class: map(p) -> x</span> <span class="pl-s1">map</span> <span class="pl-c1">=</span> <span class="pl-s1">nm</span>.<span class="pl-c1">system</span>.<span class="pl-c1">Node</span>(<span class="pl-s1">func</span>, [<span class="pl-s">'p'</span>], [<span class="pl-s">'x'</span>], <span class="pl-s1">name</span><span class="pl-c1">=</span><span class="pl-s">'map'</span>) <span class="pl-c"># define decision variables</span> <span class="pl-s1">x</span> <span class="pl-c1">=</span> <span class="pl-s1">nm</span>.<span class="pl-c1">constraint</span>.<span class="pl-c1">variable</span>(<span class="pl-s">"x"</span>)[:, [<span class="pl-c1">0</span>]] <span class="pl-s1">y</span> <span class="pl-c1">=</span> <span class="pl-s1">nm</span>.<span class="pl-c1">constraint</span>.<span class="pl-c1">variable</span>(<span class="pl-s">"x"</span>)[:, [<span class="pl-c1">1</span>]] <span class="pl-c"># problem parameters sampled in the dataset</span> <span class="pl-s1">p</span> <span class="pl-c1">=</span> <span class="pl-s1">nm</span>.<span class="pl-c1">constraint</span>.<span class="pl-c1">variable</span>(<span class="pl-s">'p'</span>) <span class="pl-c"># define objective function</span> <span class="pl-s1">f</span> <span class="pl-c1">=</span> (<span class="pl-c1">1</span><span class="pl-c1">-</span><span class="pl-s1">x</span>)<span class="pl-c1">**</span><span class="pl-c1">2</span> <span class="pl-c1">+</span> (<span class="pl-s1">y</span><span class="pl-c1">-</span><span class="pl-s1">x</span><span class="pl-c1">**</span><span class="pl-c1">2</span>)<span class="pl-c1">**</span><span class="pl-c1">2</span> <span class="pl-s1">obj</span> <span class="pl-c1">=</span> <span class="pl-s1">f</span>.<span class="pl-c1">minimize</span>(<span class="pl-s1">weight</span><span class="pl-c1">=</span><span class="pl-c1">1.0</span>) <span class="pl-c"># define constraints</span> <span class="pl-s1">con_1</span> <span class="pl-c1">=</span> <span class="pl-c1">100.</span><span class="pl-c1">*</span>(<span class="pl-s1">x</span> <span class="pl-c1">>=</span> <span class="pl-s1">y</span>) <span class="pl-s1">con_2</span> <span class="pl-c1">=</span> <span class="pl-c1">100.</span><span class="pl-c1">*</span>(<span class="pl-s1">x</span><span class="pl-c1">**</span><span class="pl-c1">2</span><span class="pl-c1">+</span><span class="pl-s1">y</span><span class="pl-c1">**</span><span class="pl-c1">2</span> <span class="pl-c1"><=</span> <span class="pl-s1">p</span><span class="pl-c1">**</span><span class="pl-c1">2</span>) <span class="pl-c"># create penalty method-based loss function</span> <span class="pl-s1">loss</span> <span class="pl-c1">=</span> <span class="pl-s1">nm</span>.<span class="pl-c1">loss</span>.<span class="pl-c1">PenaltyLoss</span>(<span class="pl-s1">objectives</span><span class="pl-c1">=</span>[<span class="pl-s1">obj</span>], <span class="pl-s1">constraints</span><span class="pl-c1">=</span>[<span class="pl-s1">con_1</span>, <span class="pl-s1">con_2</span>]) <span class="pl-c"># construct differentiable constrained optimization problem</span> <span class="pl-s1">problem</span> <span class="pl-c1">=</span> <span class="pl-s1">nm</span>.<span class="pl-c1">problem</span>.<span class="pl-c1">Problem</span>(<span class="pl-s1">nodes</span><span class="pl-c1">=</span>[<span class="pl-s1">map</span>], <span class="pl-s1">loss</span><span class="pl-c1">=</span><span class="pl-s1">loss</span>)</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Domain Examples</h2><a id="user-content-domain-examples" class="anchor" aria-label="Permalink: Domain Examples" href="#domain-examples"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">NeuroMANCER is built to tackle a variety of domain-specific modeling and control problems using its array of methods. Here we show how to model and control building energy systems, as well as apply load forecasting techniques.</p> <p dir="auto">For more in-depth coverage of our methods, please see our general <a href="#tutorials-on-methods-for-modeling-optimization-and-control">Tutorials</a> section below.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Energy Systems</h3><a id="user-content-energy-systems" class="anchor" aria-label="Permalink: Energy Systems" href="#energy-systems"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/domain_examples/NODE_building_dynamics.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Learning Building Thermal Dynamics using Neural ODEs </li> <li> <a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/domain_examples/NODE_RC_networks.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Multi-zone Building Thermal Dynamics Resistance-Capacitance network with Neural ODEs </li> <li> <a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/domain_examples/NODE_swing_equation.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Learning Swing Equation Dynamics using Neural ODEs </li> <li> <a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/domain_examples/DPC_building_control.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Learning to Control Indoor Air Temperature in Buildings </li> <li> <a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/domain_examples/HVAC_load_forecasting.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Energy Load Forecasting for Building with MLP and CNN Models </li> <li> <a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/domain_examples/building_load_forecasting_Transformers.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Energy Load Forecasting for Building with Transformers Model </li> <li> <a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/domain_examples/DPC_PSH.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Learning to Control Pumped-storage Hyrdoelectricity System </li> <li> <a href="https://github.com/pnnl/neuromancer/blob/master/examples/domain_examples/RL_DPC_building_control.ipynb"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Learning to Control a Building With Safe Reinforcement Learning and Differentiable Predictive Control </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Tutorials on Methods for Modeling, Optimization, and Control</h2><a id="user-content-tutorials-on-methods-for-modeling-optimization-and-control" class="anchor" aria-label="Permalink: Tutorials on Methods for Modeling, Optimization, and Control" href="#tutorials-on-methods-for-modeling-optimization-and-control"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Learning to Optimize (L2O) Parametric Programming</h3><a id="user-content-learning-to-optimize-l2o-parametric-programming" class="anchor" aria-label="Permalink: Learning to Optimize (L2O) Parametric Programming" href="#learning-to-optimize-l2o-parametric-programming"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Neuromancer allows you to formulate and solve a broad class of parametric optimization problems, leveraging machine learning to learn the solutions to such problems. <a href="https://github.com/pnnl/neuromancer/tree/develop/examples/parametric_programming">More information on Parametric programming</a></p> <ul dir="auto"> <li> <p dir="auto"><a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/parametric_programming/Part_1_basics.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 1: Learning to solve a constrained optimization problem.</p> </li> <li> <p dir="auto"><a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/parametric_programming/Part_2_pQP.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 2: Learning to solve a quadratically-constrained optimization problem.</p> </li> <li> <p dir="auto"><a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/parametric_programming/Part_3_pNLP.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 3: Learning to solve a set of 2D constrained optimization problems.</p> </li> <li> <p dir="auto"><a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/parametric_programming/Part_4_projectedGradient.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 4: Learning to solve a constrained optimization problem with the projected gradient.</p> </li> <li> <p dir="auto"><a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/parametric_programming/Part_5_cvxpy_layers.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 5: Using Cvxpylayers for differentiable projection onto the polytopic feasible set.</p> </li> <li> <p dir="auto"><a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/parametric_programming/Part_6_pQp_lopoCorrection.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 6: Learning to optimize with metric learning for Operator Splitting layers.</p> </li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Learning to Control (L2C)</h3><a id="user-content-learning-to-control-l2c" class="anchor" aria-label="Permalink: Learning to Control (L2C)" href="#learning-to-control-l2c"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Neuromancer allows you to learn control policies for a full spectrum of white/grey/black-box dynamical systems, subject to choice constraints and objective functions. <a href="https://github.com/pnnl/neuromancer/tree/develop/examples/control">More information on Differential Predictive Control</a></p> <ul dir="auto"> <li> <a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/control/Part_1_stabilize_linear_system.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 1: Learning to stabilize a linear dynamical system. </li> <li> <a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/control/Part_2_stabilize_ODE.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 2: Learning to stabilize a nonlinear differential equation. </li> <li> <a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/control/Part_3_ref_tracking_ODE.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 3: Learning to control a nonlinear differential equation. </li> <li> <a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/control/Part_4_NODE_control.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 4: Learning neural ODE model and control policy for an unknown dynamical system. </li> <li> <a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/control/Part_5_neural_Lyapunov.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 5: Learning neural Lyapunov function for a nonlinear dynamical system. </li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Function Approximation</h3><a id="user-content-function-approximation" class="anchor" aria-label="Permalink: Function Approximation" href="#function-approximation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Neuromancer is up-to-date with state-of-the-art methods. Here we showcase the powerful Kolgomorov-Arnold networks <a href="https://github.com/pnnl/neuromancer/tree/develop/examples/KANs">More information on Kolgomorov-Arnold Networks</a></p> <ul dir="auto"> <li><a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/feature/fbkans/examples/KANs/p1_fbkan_vs_kan_noise_data_1d.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 1: A comparison of KANs and FBKANs in learning a 1D multiscale function with noise</li> <li><a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/feature/fbkans/examples/KANs/p2_fbkan_vs_kan_noise_data_2d.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 2: A comparison of KANs and FBKANs in learning a 2D multiscale function with noise</li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">System Identification</h3><a id="user-content-system-identification" class="anchor" aria-label="Permalink: System Identification" href="#system-identification"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Neuromancer allows one to use machine learning, prior physics, and domain knowledge to construct data-driven models of dynamical systems given the measured observations of the system behavior. <a href="https://github.com/pnnl/neuromancer/tree/develop/examples/ODEs">More information on System ID via Neural State Space Models and ODEs</a></p> <ul dir="auto"> <li> <a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/ODEs/Part_1_NODE.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 1: Neural Ordinary Differential Equations (NODEs) </li> <li> <a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/ODEs/Part_2_param_estim_ODE.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 2: Parameter estimation of ODE system </li> <li> <a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/ODEs/Part_3_UDE.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 3: Universal Differential Equations (UDEs) </li> <li> <a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/ODEs/Part_4_nonauto_NODE.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 4: NODEs with exogenous inputs </li> <li> <a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/ODEs/Part_5_nonauto_NSSM.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 5: Neural State Space Models (NSSMs) with exogenous inputs </li> <li> <a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/ODEs/Part_6_NetworkODE.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 6: Data-driven modeling of resistance-capacitance (RC) network ODEs </li> <li> <a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/ODEs/Part_7_DeepKoopman.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 7: Deep Koopman operator </li> <li> <a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/ODEs/Part_8_nonauto_DeepKoopman.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 8: control-oriented Deep Koopman operator </li> <li> <a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/ODEs/Part_9_SINDy.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 9: Sparse Identification of Nonlinear Dynamics (SINDy) </li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Physics-Informed Neural Networks (PINNs)</h3><a id="user-content-physics-informed-neural-networks-pinns" class="anchor" aria-label="Permalink: Physics-Informed Neural Networks (PINNs)" href="#physics-informed-neural-networks-pinns"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Neuromancer's symbolic programming design is perfectly suited for solving PINNs. <a href="https://github.com/pnnl/neuromancer/tree/develop/examples/PDEs">More information on PINNs</a></p> <ul dir="auto"> <li><a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/PDEs/Part_1_PINN_DiffusionEquation.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 1: Diffusion Equation</li> <li><a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/PDEs/Part_2_PINN_BurgersEquation.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 2: Burgers' Equation</li> <li><a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/PDEs/Part_3_PINN_BurgersEquation_inverse.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 3: Burgers' Equation w/ Parameter Estimation (Inverse Problem)</li> <li><a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/PDEs/Part_4_PINN_LaplaceEquationSteadyState.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 4: Laplace's Equation (steady-state)</li> <li><a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/PDEs/Part_5_Pendulum_Stacked.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 5: Damped Pendulum (stacked PINN)</li> <li><a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/PDEs/Part_6_PINN_NavierStokesCavitySteady_KAN.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 6: Navier-Stokes equation (lid-driven cavity flow, steady-state, KAN)</li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Stochastic Differential Equations (SDEs)</h3><a id="user-content-stochastic-differential-equations-sdes" class="anchor" aria-label="Permalink: Stochastic Differential Equations (SDEs)" href="#stochastic-differential-equations-sdes"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Neuromancer has been integrated with TorchSDE to handle stochastic dynamical systems. <a href="https://github.com/pnnl/neuromancer/tree/develop/examples/SDEs">More information on SDEs</a></p> <ul dir="auto"> <li> <a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/SDEs/sde_walkthrough.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> LatentSDEs: "System Identification" of Stochastic Processes using Neuromancer x TorchSDE </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Scalability and Customization</h2><a id="user-content-scalability-and-customization" class="anchor" aria-label="Permalink: Scalability and Customization" href="#scalability-and-customization"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">PyTorch Lightning Integration</h3><a id="user-content-pytorch-lightning-integration" class="anchor" aria-label="Permalink: PyTorch Lightning Integration" href="#pytorch-lightning-integration"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We have integrated PyTorch Lightning to streamline code, enable custom training logic, support GPU and multi-GPU setups, and handle large-scale, memory-intensive learning tasks.</p> <ul dir="auto"> <li> <a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/lightning_integration_examples/Part_1_lightning_basics_tutorial.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 1: Lightning Integration Basics. </li> <li> <a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/lightning_integration_examples/Part_2_lightning_advanced_and_gpu_tutorial.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 2: Lightning Advanced Features and Automatic GPU Support. </li> <li> <a href="https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/lightning_integration_examples/other_examples/lightning_custom_training_example.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> Part 3: Defining Custom Training Logic via Lightning Modularized Code. </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Documentation and User Guides</h2><a id="user-content-documentation-and-user-guides" class="anchor" aria-label="Permalink: Documentation and User Guides" href="#documentation-and-user-guides"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The documentation for the library can be found <a href="https://pnnl.github.io/neuromancer/" rel="nofollow">online</a>. There is also an <a href="https://www.youtube.com/watch?v=YkFKz-DgC98" rel="nofollow">introduction video</a> covering the core features of the library.</p> <p dir="auto">For more information, including that for developers, please go to our <a href="/pnnl/neuromancer/blob/master/USER_GUIDE.md">Developer and User Guide</a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Community Information</h2><a id="user-content-community-information" class="anchor" aria-label="Permalink: Community Information" href="#community-information"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We welcome contributions and feedback from the open-source community!</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Contributions, Discussions, and Issues</h3><a id="user-content-contributions-discussions-and-issues" class="anchor" aria-label="Permalink: Contributions, Discussions, and Issues" href="#contributions-discussions-and-issues"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Please read the <a href="https://github.com/pnnl/neuromancer/blob/master/CONTRIBUTING.md">Community Development Guidelines</a> for further information on contributions, <a href="https://github.com/pnnl/neuromancer/discussions">discussions</a>, and <a href="https://github.com/pnnl/neuromancer/issues">Issues</a>.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Release notes</h3><a id="user-content-release-notes" class="anchor" aria-label="Permalink: Release notes" href="#release-notes"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">See the <a href="https://github.com/pnnl/neuromancer/blob/master/RELEASE_NOTES.md">Release notes</a> documenting new features.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">License</h3><a id="user-content-license" class="anchor" aria-label="Permalink: License" href="#license"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">NeuroMANCER comes with <a href="https://en.wikipedia.org/wiki/BSD_licenses" rel="nofollow">BSD license</a>. See the <a href="https://github.com/pnnl/neuromancer/blob/master/LICENSE.md">license</a> for further details.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Publications</h2><a id="user-content-publications" class="anchor" aria-label="Permalink: Publications" href="#publications"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://arxiv.org/abs/2503.19333" rel="nofollow">Ashish S. Nair, Bruno Jacob, Amanda A. Howard, Jan Drgona, Panos Stinis, E-PINNs: Epistemic Physics-Informed Neural Networks, arXiv:2503.19333</a></li> <li><a href="https://arxiv.org/abs/2410.11061" rel="nofollow">Bo Tang, Elias B. Khalil, Ján Drgoňa, Learning to Optimize for Mixed-Integer Non-linear Programming, arXiv:2410.11061, 2024</a></li> <li><a href="https://arxiv.org/abs/2409.13817" rel="nofollow">John Viljoen, Wenceslao Shaw Cortez, Jan Drgona, Sebastian East, Masayoshi Tomizuka, Draguna Vrabie, Differentiable Predictive Control for Robotics: A Data-Driven Predictive Safety Filter Approach, arXiv:2409.13817, 2024</a></li> <li><a href="https://ieeexplore.ieee.org/abstract/document/10479163" rel="nofollow">Jan Drgona, Aaron Tuor, Draguna Vrabie, Learning Constrained Parametric Differentiable Predictive Control Policies With Guarantees, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2024</a></li> <li><a href="https://arxiv.org/abs/2406.10433" rel="nofollow">Renukanandan Tumu, Wenceslao Shaw Cortez, Ján Drgoňa, Draguna L. Vrabie, Sonja Glavaski, Differentiable Predictive Control for Large-Scale Urban Road Networks, arXiv:2406.10433, 2024</a></li> <li><a href="https://arxiv.org/abs/2404.00882" rel="nofollow">Ethan King, James Kotary, Ferdinando Fioretto, Jan Drgona, Metric Learning to Accelerate Convergence of Operator Splitting Methods for Differentiable Parametric Programming, arXiv:2404.00882, 2024</a></li> <li><a href="https://arxiv.org/abs/2403.12938" rel="nofollow">James Koch, Madelyn Shapiro, Himanshu Sharma, Draguna Vrabie, Jan Drgona, Neural Differential Algebraic Equations, arXiv:2403.12938, 2024</a></li> <li><a href="https://arxiv.org/abs/2311.08496" rel="nofollow">Wenceslao Shaw Cortez, Jan Drgona, Draguna Vrabie, Mahantesh Halappanavar, A Robust, Efficient Predictive Safety Filter, arXiv:2311.08496, 2024</a></li> <li><a href="https://ieeexplore.ieee.org/abstract/document/10253098" rel="nofollow">Shrirang Abhyankar, Jan Drgona, Andrew August, Elliott Skomski, Aaron Tuor, Neuro-physical dynamic load modeling using differentiable parametric optimization, 2023 IEEE Power & Energy Society General Meeting (PESGM), 2023</a></li> <li><a href="https://aps.arxiv.org/abs/2207.04962" rel="nofollow">James Koch, Zhao Chen, Aaron Tuor, Jan Drgona, Draguna Vrabie, Structural Inference of Networked Dynamical Systems with Universal Differential Equations, arXiv:2207.04962, (2022)</a></li> <li><a href="https://www.sciencedirect.com/science/article/pii/S2405896322015877" rel="nofollow">Ján Drgoňa, Sayak Mukherjee, Aaron Tuor, Mahantesh Halappanavar, Draguna Vrabie, Learning Stochastic Parametric Differentiable Predictive Control Policies, IFAC ROCOND conference (2022)</a></li> <li><a href="https://arxiv.org/abs/2205.10728" rel="nofollow">Sayak Mukherjee, Ján Drgoňa, Aaron Tuor, Mahantesh Halappanavar, Draguna Vrabie, Neural Lyapunov Differentiable Predictive Control, IEEE Conference on Decision and Control Conference 2022</a></li> <li><a href="https://arxiv.org/abs/2208.02319" rel="nofollow">Wenceslao Shaw Cortez, Jan Drgona, Aaron Tuor, Mahantesh Halappanavar, Draguna Vrabie, Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach, IEEE Conference on Decision and Control Conference 2022</a></li> <li><a href="https://ieeexplore.ieee.org/document/9867379" rel="nofollow">Ethan King, Jan Drgona, Aaron Tuor, Shrirang Abhyankar, Craig Bakker, Arnab Bhattacharya, Draguna Vrabie, Koopman-based Differentiable Predictive Control for the Dynamics-Aware Economic Dispatch Problem, 2022 American Control Conference (ACC)</a></li> <li><a href="https://www.sciencedirect.com/science/article/pii/S0378778821002760" rel="nofollow">Drgoňa, J., Tuor, A. R., Chandan, V., & Vrabie, D. L., Physics-constrained deep learning of multi-zone building thermal dynamics. Energy and Buildings, 243, 110992, (2021)</a></li> <li><a href="https://ieeexplore.ieee.org/document/9482930" rel="nofollow">E. Skomski, S. Vasisht, C. Wight, A. Tuor, J. Drgoňa and D. Vrabie, "Constrained Block Nonlinear Neural Dynamical Models," 2021 American Control Conference (ACC), 2021, pp. 3993-4000, doi: 10.23919/ACC50511.2021.9482930.</a></li> <li><a href="https://proceedings.mlr.press/v144/skomski21a.html" rel="nofollow">Skomski, E., Drgoňa, J., & Tuor, A. (2021, May). Automating Discovery of Physics-Informed Neural State Space Models via Learning and Evolution. In Learning for Dynamics and Control (pp. 980-991). PMLR.</a></li> <li><a href="https://www.sciencedirect.com/science/article/pii/S2405896321012933" rel="nofollow">Drgoňa, J., Tuor, A., Skomski, E., Vasisht, S., & Vrabie, D. (2021). Deep Learning Explicit Differentiable Predictive Control Laws for Buildings. IFAC-PapersOnLine, 54(6), 14-19.</a></li> <li><a href="https://arxiv.org/abs/2004.10883" rel="nofollow">Tuor, A., Drgona, J., & Vrabie, D. (2020). Constrained neural ordinary differential equations with stability guarantees. arXiv preprint arXiv:2004.10883.</a></li> <li><a href="https://www.sciencedirect.com/science/article/pii/S0959152422000981" rel="nofollow">Drgona, Jan, et al. "Differentiable Predictive Control: An MPC Alternative for Unknown Nonlinear Systems using Constrained Deep Learning." Journal of Process Control Volume 116, August 2022, Pages 80-92</a></li> <li><a href="https://ieeexplore.ieee.org/document/9809789" rel="nofollow">Drgona, J., Skomski, E., Vasisht, S., Tuor, A., & Vrabie, D. (2020). Dissipative Deep Neural Dynamical Systems, in IEEE Open Journal of Control Systems, vol. 1, pp. 100-112, 2022</a></li> <li><a href="https://arxiv.org/abs/2004.11184" rel="nofollow">Drgona, J., Tuor, A., & Vrabie, D., Learning Constrained Adaptive Differentiable Predictive Control Policies With Guarantees, arXiv preprint arXiv:2004.11184, (2020)</a></li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Cite as</h2><a id="user-content-cite-as" class="anchor" aria-label="Permalink: Cite as" href="#cite-as"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-yaml notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="@article{Neuromancer2023, title={{NeuroMANCER: Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations}}, author={Drgona, Jan and Tuor, Aaron and Koch, James and Shapiro, Madelyn and Jacob, Bruno and Vrabie, Draguna}, Url= {https://github.com/pnnl/neuromancer}, year={2023} }"><pre><span class="pl-s">@article{Neuromancer2023,</span> <span class="pl-ent">title={{NeuroMANCER</span>: <span class="pl-s">Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations}},</span> <span class="pl-s">author={Drgona, Jan and Tuor, Aaron and Koch, James and Shapiro, Madelyn and Jacob, Bruno and Vrabie, Draguna},</span> <span class="pl-s">Url= {https://github.com/pnnl/neuromancer}, </span> <span class="pl-s">year={2023}</span> <span class="pl-s">}</span></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Development team</h2><a id="user-content-development-team" class="anchor" aria-label="Permalink: Development team" href="#development-team"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><strong>Lead developers</strong>: <a href="https://drgona.github.io/" rel="nofollow">Jan Drgona</a>, <a href="https://sw.cs.wwu.edu/~tuora/aarontuor/" rel="nofollow">Aaron Tuor</a> <strong>Active core developers</strong>: <a href="https://www.linkedin.com/in/rahul-birmiwal009/" rel="nofollow">Rahul Birmiwal</a>, <a href="https://brunopjacob.github.io/" rel="nofollow">Bruno Jacob</a>, <a href="https://reillyraab.com/about.html" rel="nofollow">Reilly Raab</a><br> <strong>Notable contributors</strong>: Madelyn Shapiro, James Koch, Seth Briney, Bo Tang, Ethan King, Elliot Skomski, Zhao Chen, Christian Møldrup Legaard<br> <strong>Scientific advisors</strong>: Draguna Vrabie, Panos Stinis</p> <p dir="auto">Open-source contributions made by:<br> <a href="https://github.com/pnnl/neuromancer/graphs/contributors"> <img src="https://camo.githubusercontent.com/0d284ec35c6d988e07244ebc90b59f0f6b4d6ec9e952b12856140d97b15b9518/68747470733a2f2f636f6e747269622e726f636b732f696d6167653f7265706f3d706e6e6c2f6e6575726f6d616e636572" data-canonical-src="https://contrib.rocks/image?repo=pnnl/neuromancer" style="max-width: 100%;"> </a></p> <p dir="auto">Made with <a href="https://contrib.rocks" rel="nofollow">contrib.rocks</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Acknowledgments</h2><a id="user-content-acknowledgments" class="anchor" aria-label="Permalink: Acknowledgments" href="#acknowledgments"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This research was partially supported by the Mathematics for Artificial Reasoning in Science (MARS) and Data Model Convergence (DMC) initiatives via the Laboratory Directed Research and Development (LDRD) investments at Pacific Northwest National Laboratory (PNNL), by the U.S. Department of Energy, through the Office of Advanced Scientific Computing Research's “Data-Driven Decision Control for Complex Systems (DnC2S)” project, and through the Energy Efficiency and Renewable Energy, Building Technologies Office under the “Dynamic decarbonization through autonomous physics-centric deep learning and optimization of building operations” and the “Advancing Market-Ready Building Energy Management by Cost-Effective Differentiable Predictive Control” projects. This project was also supported from the U.S. Department of Energy, Advanced Scientific Computing Research program, under the Uncertainty Quantification for Multifidelity Operator Learning (MOLUcQ) project (Project No. 81739). PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL0-1830.</p> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="TT3MWrA6YWfFiGhM3fzN3YcGLdqkWl2woXRNnx2+iahCr9B5KRzw48lPNbB6ziLouklzxi0+lV+w44IGtjqPpA==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control. </p> <div class="my-3 d-flex flex-items-center"> <svg aria-hidden="true" height="16" viewBox="0 0 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class="my-3"> <div class="f6"> <a href="/topics/deep-learning" title="Topic: deep-learning" data-view-component="true" class="topic-tag topic-tag-link"> deep-learning </a> <a href="/topics/pytorch" title="Topic: pytorch" data-view-component="true" class="topic-tag topic-tag-link"> pytorch </a> <a href="/topics/constrained-optimization" title="Topic: constrained-optimization" data-view-component="true" class="topic-tag topic-tag-link"> constrained-optimization </a> <a href="/topics/dynamical-systems" title="Topic: dynamical-systems" data-view-component="true" class="topic-tag topic-tag-link"> dynamical-systems </a> <a href="/topics/control-systems" title="Topic: control-systems" data-view-component="true" class="topic-tag topic-tag-link"> control-systems </a> <a href="/topics/nonlinear-dynamics" title="Topic: nonlinear-dynamics" data-view-component="true" class="topic-tag topic-tag-link"> nonlinear-dynamics </a> <a href="/topics/nonlinear-optimization" title="Topic: 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0 0 1 .673.418l1.882 3.815 4.21.612a.75.75 0 0 1 .416 1.279l-3.046 2.97.719 4.192a.751.751 0 0 1-1.088.791L8 12.347l-3.766 1.98a.75.75 0 0 1-1.088-.79l.72-4.194L.818 6.374a.75.75 0 0 1 .416-1.28l4.21-.611L7.327.668A.75.75 0 0 1 8 .25Zm0 2.445L6.615 5.5a.75.75 0 0 1-.564.41l-3.097.45 2.24 2.184a.75.75 0 0 1 .216.664l-.528 3.084 2.769-1.456a.75.75 0 0 1 .698 0l2.77 1.456-.53-3.084a.75.75 0 0 1 .216-.664l2.24-2.183-3.096-.45a.75.75 0 0 1-.564-.41L8 2.694Z"></path> </svg> <strong>1.1k</strong> stars</a> </div> <h3 class="sr-only">Watchers</h3> <div class="mt-2"> <a href="/pnnl/neuromancer/watchers" data-view-component="true" class="Link Link--muted"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-eye mr-2"> <path d="M8 2c1.981 0 3.671.992 4.933 2.078 1.27 1.091 2.187 2.345 2.637 3.023a1.62 1.62 0 0 1 0 1.798c-.45.678-1.367 1.932-2.637 3.023C11.67 13.008 9.981 14 8 14c-1.981 0-3.671-.992-4.933-2.078C1.797 10.83.88 9.576.43 8.898a1.62 1.62 0 0 1 0-1.798c.45-.677 1.367-1.931 2.637-3.022C4.33 2.992 6.019 2 8 2ZM1.679 7.932a.12.12 0 0 0 0 .136c.411.622 1.241 1.75 2.366 2.717C5.176 11.758 6.527 12.5 8 12.5c1.473 0 2.825-.742 3.955-1.715 1.124-.967 1.954-2.096 2.366-2.717a.12.12 0 0 0 0-.136c-.412-.621-1.242-1.75-2.366-2.717C10.824 4.242 9.473 3.5 8 3.5c-1.473 0-2.825.742-3.955 1.715-1.124.967-1.954 2.096-2.366 2.717ZM8 10a2 2 0 1 1-.001-3.999A2 2 0 0 1 8 10Z"></path> </svg> <strong>26</strong> watching</a> </div> <h3 class="sr-only">Forks</h3> <div class="mt-2"> <a href="/pnnl/neuromancer/forks" data-view-component="true" class="Link Link--muted"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo-forked mr-2"> <path d="M5 5.372v.878c0 .414.336.75.75.75h4.5a.75.75 0 0 0 .75-.75v-.878a2.25 2.25 0 1 1 1.5 0v.878a2.25 2.25 0 0 1-2.25 2.25h-1.5v2.128a2.251 2.251 0 1 1-1.5 0V8.5h-1.5A2.25 2.25 0 0 1 3.5 6.25v-.878a2.25 2.25 0 1 1 1.5 0ZM5 3.25a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Zm6.75.75a.75.75 0 1 0 0-1.5.75.75 0 0 0 0 1.5Zm-3 8.75a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Z"></path> </svg> <strong>145</strong> forks</a> </div> <div class="mt-2"> <a class="Link--muted" href="/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2Fpnnl%2Fneuromancer&report=pnnl+%28user%29"> Report repository </a> </div> </div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame"> <a href="/pnnl/neuromancer/releases" data-view-component="true" class="Link--primary no-underline Link">Releases <span title="5" data-view-component="true" class="Counter">5</span></a></h2> <a class="Link--primary d-flex no-underline" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/pnnl/neuromancer/releases/tag/v1.5.3"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-tag flex-shrink-0 mt-1 color-fg-success"> <path d="M1 7.775V2.75C1 1.784 1.784 1 2.75 1h5.025c.464 0 .91.184 1.238.513l6.25 6.25a1.75 1.75 0 0 1 0 2.474l-5.026 5.026a1.75 1.75 0 0 1-2.474 0l-6.25-6.25A1.752 1.752 0 0 1 1 7.775Zm1.5 0c0 .066.026.13.073.177l6.25 6.25a.25.25 0 0 0 .354 0l5.025-5.025a.25.25 0 0 0 0-.354l-6.25-6.25a.25.25 0 0 0-.177-.073H2.75a.25.25 0 0 0-.25.25ZM6 5a1 1 0 1 1 0 2 1 1 0 0 1 0-2Z"></path> </svg> <div class="ml-2 min-width-0"> <div class="d-flex"> <span class="css-truncate css-truncate-target text-bold mr-2" style="max-width: none;">v1.5.3</span> <span title="Label: Latest" data-view-component="true" class="Label Label--success flex-shrink-0"> Latest </span> </div> <div class="text-small color-fg-muted"><relative-time datetime="2025-02-26T20:41:13Z" class="no-wrap">Feb 26, 2025</relative-time></div> </div> </a> <div data-view-component="true" class="mt-3"> <a 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1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> <div class="octocat-spinner my-6 js-details-dialog-spinner"></div> </details-dialog> </details> </template> <div class="Popover js-hovercard-content position-absolute" style="display: none; outline: none;"> <div class="Popover-message Popover-message--bottom-left Popover-message--large Box color-shadow-large" style="width:360px;"> </div> </div> <template id="snippet-clipboard-copy-button"> <div class="zeroclipboard-container position-absolute right-0 top-0"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn js-clipboard-copy m-2 p-0" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon m-2"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none m-2"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> <template id="snippet-clipboard-copy-button-unpositioned"> <div class="zeroclipboard-container"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn btn-invisible js-clipboard-copy m-2 p-0 d-flex flex-justify-center flex-items-center" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> </div> <div id="js-global-screen-reader-notice" class="sr-only mt-n1" aria-live="polite" aria-atomic="true" ></div> <div id="js-global-screen-reader-notice-assertive" class="sr-only mt-n1" aria-live="assertive" aria-atomic="true"></div> </body> </html>