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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.00568">arXiv:2407.00568</a> <span> [<a href="https://arxiv.org/pdf/2407.00568">pdf</a>, <a href="https://arxiv.org/format/2407.00568">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Divide And Conquer: Learning Chaotic Dynamical Systems With Multistep Penalty Neural Ordinary Differential Equations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chakraborty%2C+D">Dibyajyoti Chakraborty</a>, <a href="/search/cs?searchtype=author&query=Chung%2C+S+W">Seung Whan Chung</a>, <a href="/search/cs?searchtype=author&query=Arcomano%2C+T">Troy Arcomano</a>, <a href="/search/cs?searchtype=author&query=Maulik%2C+R">Romit Maulik</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.00568v5-abstract-short" style="display: inline;"> Forecasting high-dimensional dynamical systems is a fundamental challenge in various fields, such as geosciences and engineering. Neural Ordinary Differential Equations (NODEs), which combine the power of neural networks and numerical solvers, have emerged as a promising algorithm for forecasting complex nonlinear dynamical systems. However, classical techniques used for NODE training are ineffect… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.00568v5-abstract-full').style.display = 'inline'; document.getElementById('2407.00568v5-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.00568v5-abstract-full" style="display: none;"> Forecasting high-dimensional dynamical systems is a fundamental challenge in various fields, such as geosciences and engineering. Neural Ordinary Differential Equations (NODEs), which combine the power of neural networks and numerical solvers, have emerged as a promising algorithm for forecasting complex nonlinear dynamical systems. However, classical techniques used for NODE training are ineffective for learning chaotic dynamical systems. In this work, we propose a novel NODE-training approach that allows for robust learning of chaotic dynamical systems. Our method addresses the challenges of non-convexity and exploding gradients associated with underlying chaotic dynamics. Training data trajectories from such systems are split into multiple, non-overlapping time windows. In addition to the deviation from the training data, the optimization loss term further penalizes the discontinuities of the predicted trajectory between the time windows. The window size is selected based on the fastest Lyapunov time scale of the system. Multi-step penalty(MP) method is first demonstrated on Lorenz equation, to illustrate how it improves the loss landscape and thereby accelerates the optimization convergence. MP method can optimize chaotic systems in a manner similar to least-squares shadowing with significantly lower computational costs. Our proposed algorithm, denoted the Multistep Penalty NODE, is applied to chaotic systems such as the Kuramoto-Sivashinsky equation, the two-dimensional Kolmogorov flow, and ERA5 reanalysis data for the atmosphere. It is observed that MP-NODE provide viable performance for such chaotic systems, not only for short-term trajectory predictions but also for invariant statistics that are hallmarks of the chaotic nature of these dynamics. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.00568v5-abstract-full').style.display = 'none'; document.getElementById('2407.00568v5-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">25 pages, 17 Figures, submitted to Computer Methods in Applied Mechanics and Engineering</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.19518">arXiv:2405.19518</a> <span> [<a href="https://arxiv.org/pdf/2405.19518">pdf</a>, <a href="https://arxiv.org/format/2405.19518">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Atmospheric and Oceanic Physics">physics.ao-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chaotic Dynamics">nlin.CD</span> </div> </div> <p class="title is-5 mathjax"> Prediction Beyond the Medium Range with an Atmosphere-Ocean Model that Combines Physics-based Modeling and Machine Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Patel%2C+D">Dhruvit Patel</a>, <a href="/search/cs?searchtype=author&query=Arcomano%2C+T">Troy Arcomano</a>, <a href="/search/cs?searchtype=author&query=Hunt%2C+B">Brian Hunt</a>, <a href="/search/cs?searchtype=author&query=Szunyogh%2C+I">Istvan Szunyogh</a>, <a href="/search/cs?searchtype=author&query=Ott%2C+E">Edward Ott</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.19518v2-abstract-short" style="display: inline;"> This paper explores the potential of a hybrid modeling approach that combines machine learning (ML) with conventional physics-based modeling for weather prediction beyond the medium range. It extends the work of Arcomano et al. (2022), which tested the approach for short- and medium-range weather prediction, and the work of Arcomano et al. (2023), which investigated its potential for climate model… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.19518v2-abstract-full').style.display = 'inline'; document.getElementById('2405.19518v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.19518v2-abstract-full" style="display: none;"> This paper explores the potential of a hybrid modeling approach that combines machine learning (ML) with conventional physics-based modeling for weather prediction beyond the medium range. It extends the work of Arcomano et al. (2022), which tested the approach for short- and medium-range weather prediction, and the work of Arcomano et al. (2023), which investigated its potential for climate modeling. The hybrid model used for the forecast experiments of the paper is based on the low-resolution, simplified parameterization atmospheric general circulation model SPEEDY. In addition to the hybridized prognostic variables of SPEEDY, the model has three purely ML-based prognostic variables: the 6h cumulative precipitation, the sea surface temperature, and the heat content of the top 300m deep layer of the ocean (a new addition compared to the model used in Arcomano et al., 2023). The model has skill in predicting the El Nino cycle and its global teleconnections with precipitation for 3-7 months depending on the season. The model captures equatorial variability of the precipitation associated with Kelvin and Rossby waves and MJO. Predictions of the precipitation in the equatorial region have skill for 15 days in the East Pacific and 11.5 days in the West Pacific. Though the model has low spatial resolution, for these tasks it has prediction skill comparable to what has been published for high-resolution, purely physics-based, conventional, operational forecast models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.19518v2-abstract-full').style.display = 'none'; document.getElementById('2405.19518v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.16297">arXiv:2405.16297</a> <span> [<a href="https://arxiv.org/pdf/2405.16297">pdf</a>, <a href="https://arxiv.org/format/2405.16297">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Atmospheric and Oceanic Physics">physics.ao-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> </div> </div> <p class="title is-5 mathjax"> LUCIE: A Lightweight Uncoupled ClImate Emulator with long-term stability and physical consistency for O(1000)-member ensembles </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Guan%2C+H">Haiwen Guan</a>, <a href="/search/cs?searchtype=author&query=Arcomano%2C+T">Troy Arcomano</a>, <a href="/search/cs?searchtype=author&query=Chattopadhyay%2C+A">Ashesh Chattopadhyay</a>, <a href="/search/cs?searchtype=author&query=Maulik%2C+R">Romit Maulik</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.16297v2-abstract-short" style="display: inline;"> We present a lightweight, easy-to-train, low-resolution, fully data-driven climate emulator, LUCIE, that can be trained on as low as $2$ years of $6$-hourly ERA5 data. Unlike most state-of-the-art AI weather models, LUCIE remains stable and physically consistent for $100$ years of autoregressive simulation with $100$ ensemble members. Long-term mean climatology from LUCIE's simulation of temperatu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.16297v2-abstract-full').style.display = 'inline'; document.getElementById('2405.16297v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.16297v2-abstract-full" style="display: none;"> We present a lightweight, easy-to-train, low-resolution, fully data-driven climate emulator, LUCIE, that can be trained on as low as $2$ years of $6$-hourly ERA5 data. Unlike most state-of-the-art AI weather models, LUCIE remains stable and physically consistent for $100$ years of autoregressive simulation with $100$ ensemble members. Long-term mean climatology from LUCIE's simulation of temperature, wind, precipitation, and humidity matches that of ERA5 data, along with the variability. We further demonstrate how well extreme weather events and their return periods can be estimated from a large ensemble of long-term simulations. We further discuss an improved training strategy with a hard-constrained first-order integrator to suppress autoregressive error growth, a novel spectral regularization strategy to better capture fine-scale dynamics, and finally an optimization algorithm that enables data-limited (as low as $2$ years of $6$-hourly data) training of the emulator without losing stability and physical consistency. Finally, we provide a scaling experiment to compare the long-term bias of LUCIE with respect to the number of training samples. Importantly, LUCIE is an easy to use model that can be trained in just $2.4$h on a single A-100 GPU, allowing for multiple experiments that can explore important scientific questions that could be answered with large ensembles of long-term simulations, e.g., the impact of different variables on the simulation, dynamic response to external forcing, and estimation of extreme weather events, amongst others. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.16297v2-abstract-full').style.display = 'none'; document.getElementById('2405.16297v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.03876">arXiv:2312.03876</a> <span> [<a href="https://arxiv.org/pdf/2312.03876">pdf</a>, <a href="https://arxiv.org/format/2312.03876">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Atmospheric and Oceanic Physics">physics.ao-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Scaling transformer neural networks for skillful and reliable medium-range weather forecasting </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Nguyen%2C+T">Tung Nguyen</a>, <a href="/search/cs?searchtype=author&query=Shah%2C+R">Rohan Shah</a>, <a href="/search/cs?searchtype=author&query=Bansal%2C+H">Hritik Bansal</a>, <a href="/search/cs?searchtype=author&query=Arcomano%2C+T">Troy Arcomano</a>, <a href="/search/cs?searchtype=author&query=Maulik%2C+R">Romit Maulik</a>, <a href="/search/cs?searchtype=author&query=Kotamarthi%2C+V">Veerabhadra Kotamarthi</a>, <a href="/search/cs?searchtype=author&query=Foster%2C+I">Ian Foster</a>, <a href="/search/cs?searchtype=author&query=Madireddy%2C+S">Sandeep Madireddy</a>, <a href="/search/cs?searchtype=author&query=Grover%2C+A">Aditya Grover</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.03876v2-abstract-short" style="display: inline;"> Weather forecasting is a fundamental problem for anticipating and mitigating the impacts of climate change. Recently, data-driven approaches for weather forecasting based on deep learning have shown great promise, achieving accuracies that are competitive with operational systems. However, those methods often employ complex, customized architectures without sufficient ablation analysis, making it… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.03876v2-abstract-full').style.display = 'inline'; document.getElementById('2312.03876v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.03876v2-abstract-full" style="display: none;"> Weather forecasting is a fundamental problem for anticipating and mitigating the impacts of climate change. Recently, data-driven approaches for weather forecasting based on deep learning have shown great promise, achieving accuracies that are competitive with operational systems. However, those methods often employ complex, customized architectures without sufficient ablation analysis, making it difficult to understand what truly contributes to their success. Here we introduce Stormer, a simple transformer model that achieves state-of-the-art performance on weather forecasting with minimal changes to the standard transformer backbone. We identify the key components of Stormer through careful empirical analyses, including weather-specific embedding, randomized dynamics forecast, and pressure-weighted loss. At the core of Stormer is a randomized forecasting objective that trains the model to forecast the weather dynamics over varying time intervals. During inference, this allows us to produce multiple forecasts for a target lead time and combine them to obtain better forecast accuracy. On WeatherBench 2, Stormer performs competitively at short to medium-range forecasts and outperforms current methods beyond 7 days, while requiring orders-of-magnitude less training data and compute. Additionally, we demonstrate Stormer's favorable scaling properties, showing consistent improvements in forecast accuracy with increases in model size and training tokens. Code and checkpoints are available at https://github.com/tung-nd/stormer. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.03876v2-abstract-full').style.display = 'none'; document.getElementById('2312.03876v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Neural Information Processing Systems (NeurIPS 2024)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.04610">arXiv:2310.04610</a> <span> [<a href="https://arxiv.org/pdf/2310.04610">pdf</a>, <a href="https://arxiv.org/format/2310.04610">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Song%2C+S+L">Shuaiwen Leon Song</a>, <a href="/search/cs?searchtype=author&query=Kruft%2C+B">Bonnie Kruft</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+M">Minjia Zhang</a>, <a href="/search/cs?searchtype=author&query=Li%2C+C">Conglong Li</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+S">Shiyang Chen</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+C">Chengming Zhang</a>, <a href="/search/cs?searchtype=author&query=Tanaka%2C+M">Masahiro Tanaka</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+X">Xiaoxia Wu</a>, <a href="/search/cs?searchtype=author&query=Rasley%2C+J">Jeff Rasley</a>, <a href="/search/cs?searchtype=author&query=Awan%2C+A+A">Ammar Ahmad Awan</a>, <a href="/search/cs?searchtype=author&query=Holmes%2C+C">Connor Holmes</a>, <a href="/search/cs?searchtype=author&query=Cai%2C+M">Martin Cai</a>, <a href="/search/cs?searchtype=author&query=Ghanem%2C+A">Adam Ghanem</a>, <a href="/search/cs?searchtype=author&query=Zhou%2C+Z">Zhongzhu Zhou</a>, <a href="/search/cs?searchtype=author&query=He%2C+Y">Yuxiong He</a>, <a href="/search/cs?searchtype=author&query=Luferenko%2C+P">Pete Luferenko</a>, <a href="/search/cs?searchtype=author&query=Kumar%2C+D">Divya Kumar</a>, <a href="/search/cs?searchtype=author&query=Weyn%2C+J">Jonathan Weyn</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+R">Ruixiong Zhang</a>, <a href="/search/cs?searchtype=author&query=Klocek%2C+S">Sylwester Klocek</a>, <a href="/search/cs?searchtype=author&query=Vragov%2C+V">Volodymyr Vragov</a>, <a href="/search/cs?searchtype=author&query=AlQuraishi%2C+M">Mohammed AlQuraishi</a>, <a href="/search/cs?searchtype=author&query=Ahdritz%2C+G">Gustaf Ahdritz</a>, <a href="/search/cs?searchtype=author&query=Floristean%2C+C">Christina Floristean</a>, <a href="/search/cs?searchtype=author&query=Negri%2C+C">Cristina Negri</a> , et al. (67 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.04610v2-abstract-short" style="display: inline;"> In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scientific exploration, bringing significant advancements across sectors from drug development to renewable energy. To answer this call, we present DeepSpeed4Science initiative (deepspeed4science.ai) which aims to build unique… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.04610v2-abstract-full').style.display = 'inline'; document.getElementById('2310.04610v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.04610v2-abstract-full" style="display: none;"> In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scientific exploration, bringing significant advancements across sectors from drug development to renewable energy. To answer this call, we present DeepSpeed4Science initiative (deepspeed4science.ai) which aims to build unique capabilities through AI system technology innovations to help domain experts to unlock today's biggest science mysteries. By leveraging DeepSpeed's current technology pillars (training, inference and compression) as base technology enablers, DeepSpeed4Science will create a new set of AI system technologies tailored for accelerating scientific discoveries by addressing their unique complexity beyond the common technical approaches used for accelerating generic large language models (LLMs). In this paper, we showcase the early progress we made with DeepSpeed4Science in addressing two of the critical system challenges in structural biology research. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.04610v2-abstract-full').style.display = 'none'; document.getElementById('2310.04610v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2002.05514">arXiv:2002.05514</a> <span> [<a href="https://arxiv.org/pdf/2002.05514">pdf</a>, <a href="https://arxiv.org/format/2002.05514">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chaotic Dynamics">nlin.CD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Atmospheric and Oceanic Physics">physics.ao-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1063/5.0005541">10.1063/5.0005541 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Combining Machine Learning with Knowledge-Based Modeling for Scalable Forecasting and Subgrid-Scale Closure of Large, Complex, Spatiotemporal Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wikner%2C+A">Alexander Wikner</a>, <a href="/search/cs?searchtype=author&query=Pathak%2C+J">Jaideep Pathak</a>, <a href="/search/cs?searchtype=author&query=Hunt%2C+B">Brian Hunt</a>, <a href="/search/cs?searchtype=author&query=Girvan%2C+M">Michelle Girvan</a>, <a href="/search/cs?searchtype=author&query=Arcomano%2C+T">Troy Arcomano</a>, <a href="/search/cs?searchtype=author&query=Szunyogh%2C+I">Istvan Szunyogh</a>, <a href="/search/cs?searchtype=author&query=Pomerance%2C+A">Andrew Pomerance</a>, <a href="/search/cs?searchtype=author&query=Ott%2C+E">Edward Ott</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2002.05514v1-abstract-short" style="display: inline;"> We consider the commonly encountered situation (e.g., in weather forecasting) where the goal is to predict the time evolution of a large, spatiotemporally chaotic dynamical system when we have access to both time series data of previous system states and an imperfect model of the full system dynamics. Specifically, we attempt to utilize machine learning as the essential tool for integrating the us… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.05514v1-abstract-full').style.display = 'inline'; document.getElementById('2002.05514v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2002.05514v1-abstract-full" style="display: none;"> We consider the commonly encountered situation (e.g., in weather forecasting) where the goal is to predict the time evolution of a large, spatiotemporally chaotic dynamical system when we have access to both time series data of previous system states and an imperfect model of the full system dynamics. Specifically, we attempt to utilize machine learning as the essential tool for integrating the use of past data into predictions. In order to facilitate scalability to the common scenario of interest where the spatiotemporally chaotic system is very large and complex, we propose combining two approaches:(i) a parallel machine learning prediction scheme; and (ii) a hybrid technique, for a composite prediction system composed of a knowledge-based component and a machine-learning-based component. We demonstrate that not only can this method combining (i) and (ii) be scaled to give excellent performance for very large systems, but also that the length of time series data needed to train our multiple, parallel machine learning components is dramatically less than that necessary without parallelization. Furthermore, considering cases where computational realization of the knowledge-based component does not resolve subgrid-scale processes, our scheme is able to use training data to incorporate the effect of the unresolved short-scale dynamics upon the resolved longer-scale dynamics ("subgrid-scale closure"). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.05514v1-abstract-full').style.display = 'none'; document.getElementById('2002.05514v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 February, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">45 pages, 15 figures</span> </p> </li> </ol> <div class="is-hidden-tablet">  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