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</script> <meta name='typesense-host' content='typesense.svc.nvidia.com'> <meta name='typesense-key' content='uFs9XGl9BWS7af7eAIbKNQ49sJnjEfQk'> <script src="https://developer.download.nvidia.com/scripts/typesense.js"></script> <script src="https://assets.adobedtm.com/5d4962a43b79/c1061d2c5e7b/launch-191c2462b890.min.js" data-ot-ignore="true"></script> <script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.6.3/jquery.min.js" integrity="sha512-STof4xm1wgkfm7heWqFJVn58Hm3EtS31XFaagaa8VMReCXAkQnJZ+jEy8PCC/iT18dFy95WcExNHFTqLyp72eQ==" crossorigin="anonymous" referrerpolicy="no-referrer"></script> <script src="https://dirms4qsy6412.cloudfront.net/assets/bootstrap/5.1.3/bootstrap.bundle.min-51ad1d8cab4ebd9873a0429f5e67ca717a71fd96daf8025bc04a88848e5b375c.js"></script> <link rel="icon" type="image/x-icon" href="https://dirms4qsy6412.cloudfront.net/assets/favicon-81bff16cada05fcff11e5711f7e6212bdc2e0a32ee57cd640a8cf66c87a6cbe6.ico" /> </head> <body class='d-flex flex-column h-100'> <div id='header'></div> <div id='page-mobile-nav-container'></div> <div class='page'> <div class="product-page"><div class="container breadcrumb-container"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="/" id="ik0k-4">Home</a></li><li class="breadcrumb-item active">NVIDIA Modulus</li></ol></div><div class="container page"><div class="row no-gutters"><div class="col-xl-9 col-lg-9 col-md-12 col-sm-12 col-main-content"><main class="page__content"><section class="page__section page__first-section"><div class="separator separator--no-scale separator--60 d-md-block d-lg-none"></div><h1 title="Introduction" class="h--large section__heading toc-item mb-0"><span id="docs-internal-guid-5e21a468-7fff-9b50-37f3-da60615c707d"><h1 id="i9qeid" class="h--largest">NVIDIA Modulus</h1></span></h1><div class="separator separator--45"></div><p id="ieweh8" class="p--large text-color-gray"><span id="docs-internal-guid-720f96e3-7fff-4bdc-6fae-43de45edfa48"><span id="ivwbsi">NVIDIA Modulus is an open-source framework for building, training, and fine-tuning Physics-ML models with a simple Python interface.</span></span></p><p id="i55w67" class="text-color-gray p--medium"><span><span id="ijfgxx"><span id="docs-internal-guid-a350e1ba-7fff-69ac-ac7f-c4e032ae1a30">Modulus empowers engineers to construct AI surrogate models that combine physics-driven causality with simulation and observed data, enabling real-time predictions. With generative AI using diffusion models, you can enhance engineering simulations and generate higher-fidelity data for scalable, responsive designs. Modulus supports the creation of large-scale digital twin models across various physics domains, from computational fluid dynamics and structural mechanics to electromagnetics.</span></span></span></p><p id="idkia3" class="text-color-gray p-medium"><span id="docs-internal-guid-b456532a-7fff-883d-fc14-a36df20eb6d5"><span id="iclt7g">Use Modulus to bolster your engineering simulations with AI. You can build models for enterprise-scale digital twin applications across multiple physics domains, from CFD and structural to electromagnetics.</span></span></p><p class="p--large text-color-gray mb-0"></p><div class="separator separator--45"></div><p class="mb-0"><a href="https://catalog.ngc.nvidia.com/orgs/nvidia/teams/modulus/containers/modulus" target="_blank" class="btn btn-cta">Download Now</a></p></section><div class="separator separator--60"></div><h2 id="i9yt8h" class="h--medium">Physics-Informed Machine Learning for Surrogate Models</h2><div class="separator separator--60"></div><p id="iocmkz"><a id="iiydp1" href="https://nvdam.widen.net/s/gfcwrp7mq2/hpc-for-dev-modulus-datasheet" target="_blank" class="link-cta text-transform-unset justify-content-center">Modulus Data Sheet</a></p><p id="ifzsw" class="mb-0"><img alt="NVIDIA Modulus framework for physics-Informed machine learning for surrogate models" src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/modulus/nvidia-modulus-850x720.svg" class="img-fluid"></p><div class="separator separator--no-scale separator--60"></div><section class="page__section page__second-section pb-0 pt-0"><h2 class="h--medium section__heading"><span id="docs-internal-guid-84b1e67a-7fff-97b5-ac80-c2782418d1d3"><span id="i9ky8v">Benefits</span></span></h2><p class="p--large text-color-gray mb-0"><span id="docs-internal-guid-d6d4778d-7fff-ac9c-f489-a773afe004b5"><span id="izqews">Modulus is an open-source, freely available AI framework for developing physics-ML models and novel AI architectures for engineering systems. </span></span></p><div class="separator separator--60 tablet-45"></div><div class="row cards-grid--60"><div class="col-md-12 col-sm-12 grid-col col-lg-3"><p id="i22q3j"><img src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/icons/m48-physics-physx.svg" id="iswnxo" alt="Decorative image of AI toolkit for physics"></p><h3 class="h--smaller mb-0"><span id="docs-internal-guid-5fd02814-7fff-9846-b56f-f3caf4b1901e"><span id="icfvre">AI Toolkit for Physics</span></span></h3><div class="separator separator--30"></div><p class="mb-0"><span id="docs-internal-guid-9588c591-7fff-aecc-ede3-5c848c793c26"><span id="i2pnvh">Quickly configure, build, and train AI models for physical systems in any domain, from engineering simulations to life sciences, with simple Python APIs.</span></span></p><div class="separator separator--30"></div></div><div class="col-md-12 col-sm-12 grid-col col-lg-3"><p id="ido5oj"><img src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/icons/m48-ai-customization.svg" id="ih40nj" alt="Decorative image of customizing models"></p><h3 class="h--smaller mb-0"><span id="docs-internal-guid-50d39afe-7fff-c75d-b5ba-adc7df9627bf"><span id="i1291d">Customize Models</span></span></h3><div class="separator separator--30"></div><p class="mb-0"><span id="docs-internal-guid-ec4fbdc7-7fff-e4cb-858a-1548ed529d1c"><span id="iofsqe"> Download, build on, and customize state-of-the-art pretrained models from the </span><a href="https://catalog.ngc.nvidia.com/" id="irnf4c"><span id="ims1ng">NVIDIA NGC™ catalog</span></a><span id="is4ym6">.</span></span></p><div class="separator separator--30"></div></div><div class="col-md-12 col-sm-12 grid-col col-lg-3"><p id="inhm01"><img src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/icons/m48-digital-twin.svg" id="i802gs" alt="Decorative image of near-real-time inference"></p><h3 class="h--smaller mb-0"><span id="docs-internal-guid-a4ea3dc7-7fff-0443-39ab-c0cad186ab69"><span id="i0jsko">Near-Real-Time Inference</span><span id="ifofdn"> </span></span></h3><div class="separator separator--30"></div><p class="mb-0"><span id="docs-internal-guid-104532dc-7fff-1b7e-9326-e86ddb5206a4"><span id="inq65g">Deploy AI surrogate models as digital twins of your physical systems to simulate in near real time.</span></span></p><div class="separator separator--30"></div></div><div class="col-md-12 col-sm-12 grid-col col-lg-3"><p id="ijgjwr"><img src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/icons/m48-neuralnetwork-2.svg" id="i2eiqf" alt="Decorative image of scaling with NVIDIA AI"></p><h3 class="h--smaller mb-0"><span id="docs-internal-guid-cfbf70fc-7fff-34ff-baf9-07859f0a163d"><span id="i3kgpk">Scale With NVIDIA AI</span></span></h3><div class="separator separator--30"></div><p class="mb-0"><span id="docs-internal-guid-150cea62-7fff-4981-26a2-e766cab29f37"><span id="i3domo">Leverage NVIDIA AI to scale training performance from a single GPU to multi-node implementations.</span></span></p><div class="separator separator--30"></div></div><div class="col-md-12 col-sm-12 grid-col col-lg-3"><p id="inemth"><img src="https://developer.download.nvidia.com/images/modulus/m48-developer-2.svg" id="iwmg5o" alt="Decorative image of open-source design"></p><h3 class="h--smaller mb-0"><span id="docs-internal-guid-ee407773-7fff-6e85-0293-5bbbb399fba0"><span id="ivpzvf">Open-Source Design</span></span></h3><div class="separator separator--30"></div><p class="mb-0"><span id="docs-internal-guid-cc78b647-7fff-810e-b3a1-7d6a184807ad"><span id="i9841j">Experience the benefits of open source. Modulus is built on top of PyTorch and is released under the Apache 2.0 license. </span></span></p><div class="separator separator--30"></div></div><div class="col-md-12 col-sm-12 grid-col col-lg-3"><p id="irw7cc"><img src="https://developer.download.nvidia.com/images/modulus/m48-check-mark-approved-1.svg" id="isuqtn" alt="Decorative image of standardized, best practices of AI development "></p><h3 class="h--smaller mb-0"><span id="docs-internal-guid-d39c97c2-7fff-935c-3c1f-3214840c8048"><span id="i30qlu">Standardized</span></span></h3><div class="separator separator--30"></div><p class="mb-0"><span id="docs-internal-guid-f1781c0b-7fff-a92c-27e1-9a984f94402e"><span id="i77b1p"> Work with the best practices of AI development for physics-ML models, with an immediate focus on engineering applications.</span></span></p><div class="separator separator--30"></div></div><div class="col-md-12 col-sm-12 grid-col col-lg-3"><p id="i1kenv"><img src="https://developer.download.nvidia.com/images/modulus/m48-service-api.svg" id="ihca49" alt=" Decorative image of user friendly API interfaces"></p><h3 class="h--smaller mb-0"><span id="docs-internal-guid-262cb550-7fff-bf4d-7a9f-89ba605cc63d"><span id="i7y4ul">User Friendly</span></span></h3><div class="separator separator--30"></div><p class="mb-0"><span id="docs-internal-guid-f008dadf-7fff-d1e2-e1b4-b5113faf1014"><span id="ijlt2h">Boost productivity with user-comprehensible error messages and easy-to-program Pythonic API interfaces.</span></span></p><div class="separator separator--30"></div></div><div class="col-md-12 col-sm-12 grid-col col-lg-3"><p id="ia3bjj"><img src="https://developer.download.nvidia.com/images/modulus/m48-configuration-sdk-2.svg" id="iu01xo" alt="Decorative image of high-quality software with enterprise-grade development"></p><h3 class="h--smaller mb-0"><span id="docs-internal-guid-fd0dd493-7fff-e64f-fe9a-5b629d52e7d4"><span id="ifdpk4">High Quality</span></span></h3><div class="separator separator--30"></div><p class="mb-0"><span id="docs-internal-guid-d95f26d0-7fff-fc46-2355-822d87685da8"><span id="ix04v1">Use high-quality software with enterprise-grade development, tutorials for getting started, and robust validation and documentation.</span></span></p><div class="separator separator--30"></div></div></div></section><hr class="separator separator--md"><section class="page__section pt-0 pb-0"><h2 title="Latest News" class="h--medium section__heading toc-item tablet-45"><span id="docs-internal-guid-b0c512ff-7fff-8ba5-b45f-acd8574982b7"><span id="i589hi">See Modulus in Action</span></span></h2></section><div class="container"><div class="row"><div class="col col-md-4"><div class="separator separator--30"></div><p id="idsfoq"></p><div class="ratio ratio-16x9"><iframe id="i3hqum" src="https://www.youtube.com/embed/9vEaImsSCrw?"></iframe></div> <p></p><h3 id="ihe695" class="h--smallest">Speed and Accuracy of Gen AI Helps Combat Climate Change</h3></div><div class="col col-md-4"><div class="separator separator--30"></div><p id="i9dte9"></p><div class="ratio ratio-16x9"><iframe id="i4fttk" src="https://www.youtube.com/embed/nuT_U1AQz3g?"></iframe></div> <p></p><h3 id="iuvppm" class="h--smallest">Accelerating Extreme Weather Prediction with FourCastNet</h3></div><div class="col col-md-4"><p id="ifqyse"></p><div class="separator separator--30"></div><div class="ratio ratio-16x9"><iframe id="i2nssq" src="https://www.youtube.com/embed/JLboPXn6sKI?"></iframe></div> <p></p><h3 id="izjtbj" class="h--smallest">Siemens Energy HRSG Digital Twin Simulation Using NVIDIA Modulus and Omniverse</h3></div></div></div><div class="separator separator--60 tablet-45"></div><div class="container"><div class="row"><div class="col col-md-4"><div class="separator separator--30"></div><p id="i5fn1a"></p><div class="ratio ratio-16x9"><iframe id="is1h67" src="https://www.youtube.com/embed/mQuvYQmdbtw?"></iframe></div> <p></p><h3 id="ijfe2j" class="h--smallest">Maximizing Wind Energy Production Using Wake Optimization</h3></div><div class="col col-md-4"><div class="separator separator--30"></div><p id="i2unpi"></p><div class="ratio ratio-16x9"><iframe id="irskmb" src="https://www.youtube.com/embed/u-M5LQvx1cQ?"></iframe></div> <p></p><h3 id="ixn1pk" class="h--smallest">Accelerating Carbon Capture and Storage with Fourier Neural Operator and NVIDIA Modulus</h3></div><div class="col col-md-4"><div class="separator separator--30"></div><p id="ixrg4p"></p><div class="ratio ratio-16x9"><iframe id="iygvkn" src="https://www.youtube.com/embed/FUUT6IrQjo4?"></iframe></div> <p></p><h3 id="iwrgtm" class="h--smallest">Predicting Extreme Weather Events Three Weeks in Advance with FourCastNet</h3></div></div></div><div class="separator separator--60 tablet-45"></div><p id="io1i8f" class="text-center"><a id="id9vjd" href="https://github.com/NVIDIA/modulus/blob/main/examples/README.md" target="_blank" class="link-cta text-transform-unset justify-content-center">Explore More</a></p><div class="separator separator--60 tablet-45"></div><section class="page__section page__last-section page__cta-section page__section--dark-gray"><h2 id="i7qmnf" class="text-center"><span id="docs-internal-guid-72841e22-7fff-e7a9-fe66-936158cf1be3"><span id="izht5x" class="text-white">Contribute to Modulus’ Development</span></span></h2><p class="p--large lead text-center mb-0"><span id="docs-internal-guid-ccc0b99c-7fff-4bb4-586c-23a34942999e-2"><span id="io8gql" class="text-white">Modulus provides a unique platform for collaboration within the scientific community. Domain experts are invited to contribute and accelerate physics-ML across a variety of use cases and applications.</span></span></p><div class="separator separator--60"></div><p class="text-center mb-0"><a href="https://github.com/NVIDIA/modulus" target="_blank" class="btn btn-cta">Go To GitHub</a></p></section><section class="page__section pt-0 pb-0"><section class="page__section page__second-section pb-0 pt-0"><div class="separator separator--60 tablet-45"></div><h2 class="h--medium section__heading toc-item"><span id="itcu6l"><span id="i2b2y3">Key Features</span></span></h2><div class="separator separator--60 tablet-45"></div><div class="row cards-grid--60"><div class="col-md-12 col-sm-12 grid-col col-lg-4"><h3 class="h--smaller mb-0"><span id="docs-internal-guid-0a2fe2a8-7fff-6920-860f-90306402187c"><span id="idpcwk" class="h--smallest">New Model Architectures</span></span></h3><div class="separator separator--30"></div><p class="mb-0"><span id="docs-internal-guid-d3b931db-7fff-ced8-2faf-09bc8691b5dd"></span></p><p id="iengtj"><span id="ifmcq9">Modulus offers a variety of approaches for training physics-based models, from purely physics-driven models like PINNs to physics-based, data-driven architectures such as neural operators, </span><span id="imt4ry"><b>GNNs, and generative AI based diffusion models.</b></span></p><span id="i51qjx">Modulus includes curated Physics-ML model architectures, Fourier feature networks, Fourier neural operators, </span><span id="iz9sfd"><b>GNNs, and diffusion models</b></span><span id="itsmm4"> </span><span id="iw2gwa">trained on</span><a href="https://www.nvidia.com/en-us/data-center/dgx-systems/" id="ifcr5i"><span id="irkuca"> NVIDIA DGX</span></a><span id="izzl3p"> across open-source, free datasets found in the </span><a href="https://docs.nvidia.com/deeplearning/modulus/index.html" id="imathi"><span id="ijhrxf">documentation</span></a><span id="isqos3">.</span><br><p></p><div class="separator separator--30"></div></div><div class="col-md-12 col-sm-12 grid-col col-lg-4"><h3 class="h--smaller mb-0"><span id="docs-internal-guid-0cee6a35-7fff-81a9-8e3c-07d117f2b739"><span id="iii28t" class="h--smallest">Training State-of-the-Art Physics-ML Models</span></span></h3><div class="separator separator--30"></div><p class="mb-0"><span id="docs-internal-guid-f9ff4d70-7fff-e65f-58e8-3fa1b7bbf050"></span></p><p id="i0md7h"><span id="iwo3pt">Modulus provides an end-to-end pipeline for training Physics-ML models—from ingesting geometry to adding PDEs and scaling the training to multi-node GPUs. Modulus also includes training recipes in the form of reference applications.</span></p><a id="i44uhw" href="https://docs.nvidia.com/modulus/index.html" target="_blank" class="link-cta text-transform-unset">Documentation</a><p id="iai4fs"><a href="https://docs.nvidia.com/deeplearning/modulus/index.html" id="inhloc"></a></p><div id="i85u9u"><br></div><p></p></div><div class="col-md-12 col-sm-12 grid-col col-lg-4"><h3 class="h--smaller mb-0"><span id="docs-internal-guid-aa0247c0-7fff-9854-7a70-48bebd03717a"><span id="ir195w" class="h--smallest">Explicit Parameterization</span></span></h3><div class="separator separator--30"></div><p class="mb-0"><span id="docs-internal-guid-19edde9c-7fff-fad7-20f0-8f5f058ff174"><span id="iv2at5">Modulus provides explicit parameter specifications for training the surrogate model with a range of values to learn for the design space and for inferring multiple scenarios simultaneously.</span></span><br></p><div class="separator separator--30"></div></div></div></section><hr class="separator separator--md"><h2 id="i6tjaq" class="h--medium"><span id="docs-internal-guid-1dbf524f-7fff-36a0-75dc-f2cf97c0066b"><span id="i9t5w5">Omniverse Integration</span></span></h2><p id="i9zwnc"><span id="docs-internal-guid-1f883c77-7fff-5475-7761-ecfc33fa03b3"><span id="idgvrb">Modulus is now integrated with the NVIDIA Omniverse™ platform for connecting and building custom 3D pipelines via an extension that can be used to visualize the outputs of a Modulus-trained model. The Modulus extension enables you to import the output results into a visualization pipeline for common output scenarios, such as streamlines and iso-surfaces. It also provides an interface that enables interactive exploration of design variables and parameters for inferring new system behavior and visualizing it in near real time.</span></span></p><p id="ipbuhl"><a id="irl0of" href="https://docs.omniverse.nvidia.com/extensions/latest/ext_modulus.html" target="_blank" class="link-cta text-transform-unset">Omniverse Integration Documentation</a></p><div class="separator separator--60 tablet-45"></div><h2 id="ij9y8b" class="h--medium"><span id="docs-internal-guid-4440091e-7fff-bd8a-52fe-99bdd74289a5"><span id="i7d141">Production-Ready Solution With NVIDIA AI Enterprise</span></span></h2><p id="i4mjcv"><span id="docs-internal-guid-b86d2535-7fff-d3b2-b6d4-a3f2f51e49d6"><span id="impkf5">Modulus is now available with NVIDIA AI Enterprise, an end-to-end AI software platform optimized to accelerate enterprises to the leading edge of AI. NVIDIA AI Enterprise delivers validation and integration for NVIDIA AI open-source software, access to AI solution workflows to speed time to production, certifications to deploy AI everywhere, and enterprise-grade support, security, and API stability while mitigating the potential risks of open-source software</span></span></p><p id="iw7ofj"><a id="iwycjj" href="https://www.nvidia.com/en-us/data-center/products/ai-enterprise/" target="_blank" class="link-cta text-transform-unset">Learn more about NVIDIA AI  Enterprise</a></p><hr class="separator separator--md"><section class="page__section page__second-section pb-0 pt-0"></section><section class="page__section page__second-section pb-0 pt-0"><h2 title="Getting Started" class="h--medium section__heading toc-item"><span id="ij2kv5"><span id="iecmqy"><span id="docs-internal-guid-bc59ed59-7fff-3417-ab42-2157de47a5e0"><span id="i7itqk">Ways to Get Started With NVIDIA Modulus</span></span></span></span></h2><div class="separator separator--60 tablet-45"></div><div class="row cards-grid--60"><div class="col-md-12 col-sm-12 grid-col col-lg-4"><p id="ibic8m"><img src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/m48-ai-customization.svg" id="ihu3qq" alt="Decorative image of downloading containers and models for development"></p><h3 class="mb-0 h--smallest"><span id="docs-internal-guid-de63946b-7fff-5986-313f-e927a81da4d6"><span id="iiesev">Download Containers and Models for Development</span></span><br></h3><div class="separator separator--30"></div><p id="iklgek"><span id="docs-internal-guid-a3aab0d9-7fff-800a-4121-397e88dbfae3"><span id="iokcnb">Develop Physics-ML models using Modulus container and pretrained models, available for free on </span><a href="https://www.nvidia.com/en-us/gpu-cloud/" id="ib81c1"><span id="iwd0a1">NVIDIA NGC</span></a><span id="ikvdke">.</span></span></p><div class="separator separator--30"></div><p id="i5muxh"><a id="ih28lg" href="https://catalog.ngc.nvidia.com/orgs/nvidia/teams/modulus/containers/modulus" target="_blank" class="link-cta text-transform-unset">Download Now</a></p></div><div class="col-md-12 col-sm-12 grid-col col-lg-4"><p id="im3c4s"><img src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/m48-scalability-up-sample.svg" id="ix9k2l" alt="Decorative image of enterprise-scale workflows"></p><h3 class="mb-0 h--smallest"><span id="docs-internal-guid-51aa7c80-7fff-7350-d55a-94fe9ecf76b1"><span id="it9kql">Enterprise-Scale Workflows</span></span><br></h3><div class="separator separator--30"></div><p class="mb-0"><span></span></p><p id="ic8epq"><span id="i39d7a"><span id="docs-internal-guid-cbc2fd74-7fff-83ed-a124-27780d8afef8"><span id="istygj">Get free access to NVIDIA cloud workflows for Modulus and experience the ease of scaling to enterprise workloads.</span></span></span></p><div id="ima5ie"><br></div><p></p><p id="izpw8t"><a id="ie891j" href="https://www.nvidia.com/en-us/launchpad/ai/physics-informed-machine-learning-with-modulus/" target="_blank" class="link-cta text-transform-unset">Try on NVIDIA LaunchPad</a></p></div><div class="col-md-12 col-sm-12 grid-col col-lg-4"><p id="ig0dmb"><img src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/m48-learning-development-online-education.svg" id="i0l00l" alt=" Decorative image of self-paced online course"></p><h3 class="mb-0 h--smallest"><span id="docs-internal-guid-69628e78-7fff-8be1-2e54-d8c95de7469e"><span id="iw99lm">Self-Paced Online Course</span></span><br></h3><div class="separator separator--30"></div><p class="mb-0"><span id="docs-internal-guid-1d02d040-7fff-e171-fb4a-a9965f620aa6"><span id="innc9s">Take a hands-on introductory course from the NVIDIA Deep Learning Institute (DLI) to explore physics-informed machine learning with Modulus.</span></span><br></p><div class="separator separator--30"></div><p id="iyxar5"><a id="ijfv7u" href="https://courses.nvidia.com/courses/course-v1:DLI+S-OV-04+V1/" target="_blank" class="link-cta text-transform-unset">Access Course</a></p></div></div></section><div class="separator separator--60 tablet-45"></div><h2 id="i2fs53" class="h--medium">What Others Are Saying</h2><div class="separator separator--60 tablet-45"></div><div class="quotes-list-outer"><div class="quotes-list-viewport"><div class="quotes-list"><blockquote id="in0pak" class="quote text-center"><div class="quote__text"><p class="p--large"><span id="docs-internal-guid-3a5d9831-7fff-1368-5d48-b632946ca1ed"><span id="inz1ck">[Modulus’s] clear APIs, clean and easily navigable code, environment, and hardware configurations well handled with dockers, scalability, ease of deployment, and the competent support team made it easy to adopt and has provided some very promising results. This has been great so far, and we look forward to using [Modulus] on problems with much larger dimensions.</span></span></p></div><div class="quote__author"><p id="iwt71b">— Cedric Frances, Ph.D. Student, Stanford University</p></div><a id="ix0l6i" href="https://developer.nvidia.com/blog/using-physics-informed-deep-learning-for-transport-in-porous-media/" target="_blank">[Using Physics-Informed Deep Learning for Transport in Porous Media]</a></blockquote><blockquote id="izmycs" class="quote text-center"><div class="quote__text"><p class="p--large"><span id="docs-internal-guid-88a52d4b-7fff-3f00-c2b6-d288d3c6bf00"><span id="izqk0d">[Modulus] is an AI-based physics simulation toolkit that has the potential to unlock amazing capabilities in industrial and scientific simulation.</span></span></p></div><div class="quote__author"><p id="ifas8i">— Christopher Lamb, VP of Computing Software, NVIDIA</p><a id="iekwz3" href="https://www.youtube.com/watch?v=cNKAD4_ww_I&t=1459s" target="_blank">[The NextPlatform Video]</a></div><a id="ibwtbt"><span id="docs-internal-guid-3c83b602-7fff-8f7e-30bb-57c16d8182ba"></span></a><a href="https://www.youtube.com/watch?v=cNKAD4_ww_I&t=1459s" id="insydx"></a></blockquote><blockquote id="igc1rj" class="quote text-center"><div class="quote__text"><p class="p--large"><span id="docs-internal-guid-e9aa887d-7fff-718f-16d0-d17366948ab1"><span id="ixe6ot">We believe that [Modulus] has some unique features like parameterized geometries for multi-physics problems and multi-GPU/multi-node neural network implementation. We are looking forward to incorporating [Modulus] in our research and teaching activities.</span></span></p></div><div class="quote__author"><p id="iftvee">— Professor Hadi Meidani, Civil and Environmental Engineering, University of Illinois at Urbana-Champaign</p></div></blockquote><blockquote id="ith2sz" class="quote text-center"><div class="quote__text"><p class="p--large"><span id="docs-internal-guid-44c7a7c1-7fff-8bcc-67e0-31963a60e520"><span id="iz0c3p">The collaboration between Siemens Gamesa and NVIDIA has meant a great step forward in accelerating the computational speed and the deployment speed of our latest algorithms development in such a complex field as computational fluid dynamics.</span></span></p></div><div class="quote__author"><p id="i9ey33">— Sergio Dominguez, Siemens Gamesa</p></div><a id="ilt4qs" href="https://blogs.nvidia.com/blog/2022/03/22/siemens-gamesa-wind-farms-digital-twins/" target="_blank">[NVIDIA Blog]</a></blockquote><blockquote id="iwegjd" title="What Others Are Saying" class="quote text-center"><div class="quote__text"><p class="p--large"><span id="docs-internal-guid-68366ef3-7fff-7b8c-7be9-9d08f1b03fcd"><span id="ikao7g">Accelerated computing with AI at data center scale has the potential to deliver millionfold increases in performance to tackle challenges, such as mitigating climate change, discovering drugs, and finding new sources of renewable energy. NVIDIA’s AI-enabled framework for scientific digital twins equips researchers to pursue solutions to these massive problems.</span></span></p></div><div class="quote__author"><p id="i77drq">— Ian Buck, VP of Accelerated Computing, NVIDIA</p><a id="i7wg65" href="https://nvidianews.nvidia.com/news/nvidia-announces-digital-twin-platform-for-scientific-computing%20Show%20less" target="_blank">[NVIDIA Press Release]</a></div></blockquote></div></div></div></section><div class="separator separator--60 tablet-45"></div><section class="page__section pt-0 pb-0"><section class="page__section pt-0 pb-0"><h2 title="Higher Learning" class="h--medium section__heading toc-item">Higher Education and Research Developer Resources</h2><div class="row extra-resources"><div class="col-lg-4 col-md-4 col-sm-12"><h3 id="iea2lk" class="h--smallest">Self-Paced Online Course</h3><p id="izu348"><span id="docs-internal-guid-78829add-7fff-13cb-d4cd-7e3b7230747f"><span id="ijfpst">Take a hands-on introductory course from the NVIDIA DLI to explore physics-informed machine learning with Modulus.</span></span></p><a id="iq4kkj" href="https://courses.nvidia.com/courses/course-v1:DLI+S-OV-04+V1/" target="_blank" class="link-cta text-transform-unset">Access Course</a></div><div class="col-lg-4 col-md-4 col-sm-12"><h3 id="issqep" class="h--smallest">Teaching Kit for Educators</h3><p id="ih903g"><span id="docs-internal-guid-1081e71c-7fff-2985-c23d-f0393ec12356"><span id="ipzabw">A DLI Teaching Kit is available to qualified university educators interested in Physics-ML. Comprehensive and modular, the kit can help you integrate lecture materials, hands-on exercises, GPU cloud resources, and more into your curriculum.</span></span></p><a id="iq1vdw" href="https://www.nvidia.com/en-us/training/teaching-kits/" target="_blank" class="link-cta text-transform-unset">Access Teaching Kit</a><p id="izrokk"></p><a href="https://www.nvidia.com/en-us/on-demand/session/other2024-nvmodulus/" target="_blank" class="link-cta text-transform-unset">Watch Webinar</a></div><div class="col-lg-4 col-md-4 col-sm-12"><h3 id="ind9fh" class="h--smallest">Open Hackathons and Bootcamps</h3><p id="ibzz9q"><span id="docs-internal-guid-e37ad439-7fff-3ba8-b694-e950b12821bd"><span id="ivvkch">Accelerate and optimize research applications with mentors by your side.</span></span></p><a id="iegbm2" href="https://github.com/openhackathons-org/End-to-End-AI-for-Science#end-to-end-ai-for-science" target="_blank" class="link-cta text-transform-unset">End-to-End AI for Science Hackathon GitHub</a><p id="izqd47"></p><a id="iug87y" href="https://www.openhackathons.org/s/upcoming-events" target="_blank" class="link-cta text-transform-unset">Upcoming Open Hackathons</a></div></div></section><div class="separator separator--60 tablet-45"></div><section class="page__section pt-0 pb-0"><h2 title="Introductory Resources" class="h--medium section__heading toc-item tablet-45">Introductory Resources</h2><div class="row cards__list"><div class="col-xl-4 col-lg-4 col-md-12 col-sm-12"><div class="card-wrapper"><div class="card"><img src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/wind-turbines.jpg" id="i2s2jr" alt="wind turbines" class="img-fluid"><div class="card--content"><div class="card--text"><div class="separator separator--30"></div><h3 class="h--smallest txt-clr--blck mb-0">Using NVIDIA Modulus and Omniverse Wind Farm Digital Twin for Siemens Gamesa</h3><div class="separator separator--30"></div></div><div class="card--cta"><a href="https://resources.nvidia.com/en-us-modulus-pathfactory/siemens-gamesa-wind?lx=EOCANS" target="_blank" class="link-cta text-transform-unset fw-bold">Read Blog</a></div></div></div></div></div><div class="col-xl-4 col-lg-4 col-md-12 col-sm-12"><div class="card-wrapper"><div class="card"><img src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/digital-twin-exterior-1280x680-1.jpg" id="id4s0i" alt="Siemens Energy taps NVIDIA to develop industrial digital twin of power plant in Omniverse and Modulus" class="img-fluid"><div class="card--content"><div class="card--text"><div class="separator separator--30"></div><h3 class="h--smallest txt-clr--blck mb-0">Siemens Energy Taps NVIDIA to Develop Industrial Digital twin of Power Plant in Omniverse and Modulus</h3><div class="separator separator--30"></div></div><div class="card--cta"><a href="https://resources.nvidia.com/en-us-modulus-pathfactory/develop-industrial-digital-twin-of-power?lx=EOCANS" target="_blank" class="link-cta text-transform-unset fw-bold">Read Blog</a></div></div></div></div></div><div class="col-xl-4 col-lg-4 col-md-12 col-sm-12"><div class="card-wrapper"><div class="card"><img src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/Developing-digital-twins.jpg" id="ikqftc" alt="Watch presentation about developing Digital Twins for weather, climate, and energy" class="img-fluid"><div class="card--content"><div class="card--text"><div class="separator separator--30"></div><h3 class="h--smallest txt-clr--blck mb-0">Developing Digital Twins for Weather, Climate, and Energy</h3><div class="separator separator--30"></div></div><div class="card--cta"><a href="https://resources.nvidia.com/en-us-modulus-pathfactory/gtcspring22-s41823?lx=EOCANS" target="_blank" class="link-cta text-transform-unset fw-bold">Watch on-demand</a></div></div></div></div></div></div><div class="separator separator--30"></div></section></section><section class="page__section pt-0 pb-0"><p id="i212e9"></p><p id="iwdvzj" class="text-center"><a id="ivf1zh" href="https://resources.nvidia.com/l/en-us-modulus-pathfactory-explore-page" target="_blank" class="fw-bold link-cta text-transform-unset justify-content-center">Explore Modulus Resources</a></p><div class="separator separator--60 card__content"></div></section><h2 id="i5ie16" class="h--medium">Modulus Featured Content</h2><div class="separator separator--60 card__content"></div><div data-react-class="GalleryWidget" data-react-props="{"id":"64757d91-8da4-4d44-b097-c7371dfc5b691","widget_type":"playlist","hide_title":true,"header":"Modulus Featured Content","destination":"https://www.nvidia.com/en-us/on-demand/playlist/playList-bd07f4dc-1397-4783-a959-65cec79aa985/","playlist_id":"playList-bd07f4dc-1397-4783-a959-65cec79aa985"}" data-react-cache-id="GalleryWidget-64757d91-8da4-4d44-b097-c7371dfc5b691"></div><div class="separator separator--60"></div><section class="page__section page__last-section page__cta-section 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