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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/2411.12973">arXiv:2411.12973</a> <span> [<a href="https://arxiv.org/pdf/2411.12973">pdf</a>, <a href="https://arxiv.org/format/2411.12973">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> </div> </div> <p class="title is-5 mathjax"> Adaptive Process-Guided Learning: An Application in Predicting Lake DO Concentrations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yu%2C+R">Runlong Yu</a>, <a href="/search/cs?searchtype=author&query=Qiu%2C+C">Chonghao Qiu</a>, <a href="/search/cs?searchtype=author&query=Ladwig%2C+R">Robert Ladwig</a>, <a href="/search/cs?searchtype=author&query=Hanson%2C+P+C">Paul C. Hanson</a>, <a href="/search/cs?searchtype=author&query=Xie%2C+Y">Yiqun Xie</a>, <a href="/search/cs?searchtype=author&query=Li%2C+Y">Yanhua Li</a>, <a href="/search/cs?searchtype=author&query=Jia%2C+X">Xiaowei Jia</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="2411.12973v1-abstract-short" style="display: inline;"> This paper introduces a \textit{Process-Guided Learning (Pril)} framework that integrates physical models with recurrent neural networks (RNNs) to enhance the prediction of dissolved oxygen (DO) concentrations in lakes, which is crucial for sustaining water quality and ecosystem health. Unlike traditional RNNs, which may deliver high accuracy but often lack physical consistency and broad applicabi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12973v1-abstract-full').style.display = 'inline'; document.getElementById('2411.12973v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.12973v1-abstract-full" style="display: none;"> This paper introduces a \textit{Process-Guided Learning (Pril)} framework that integrates physical models with recurrent neural networks (RNNs) to enhance the prediction of dissolved oxygen (DO) concentrations in lakes, which is crucial for sustaining water quality and ecosystem health. Unlike traditional RNNs, which may deliver high accuracy but often lack physical consistency and broad applicability, the \textit{Pril} method incorporates differential DO equations for each lake layer, modeling it as a first-order linear solution using a forward Euler scheme with a daily timestep. However, this method is sensitive to numerical instabilities. When drastic fluctuations occur, the numerical integration is neither mass-conservative nor stable. Especially during stratified conditions, exogenous fluxes into each layer cause significant within-day changes in DO concentrations. To address this challenge, we further propose an \textit{Adaptive Process-Guided Learning (April)} model, which dynamically adjusts timesteps from daily to sub-daily intervals with the aim of mitigating the discrepancies caused by variations in entrainment fluxes. \textit{April} uses a generator-discriminator architecture to identify days with significant DO fluctuations and employs a multi-step Euler scheme with sub-daily timesteps to effectively manage these variations. We have tested our methods on a wide range of lakes in the Midwestern USA, and demonstrated robust capability in predicting DO concentrations even with limited training data. While primarily focused on aquatic ecosystems, this approach is broadly applicable to diverse scientific and engineering disciplines that utilize process-based models, such as power engineering, climate science, and biomedicine. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12973v1-abstract-full').style.display = 'none'; document.getElementById('2411.12973v1-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> 19 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.18923">arXiv:2403.18923</a> <span> [<a href="https://arxiv.org/pdf/2403.18923">pdf</a>, <a href="https://arxiv.org/format/2403.18923">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</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"> Evolution-based Feature Selection for Predicting Dissolved Oxygen Concentrations in Lakes </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yu%2C+R">Runlong Yu</a>, <a href="/search/cs?searchtype=author&query=Ladwig%2C+R">Robert Ladwig</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+X">Xiang Xu</a>, <a href="/search/cs?searchtype=author&query=Zhu%2C+P">Peijun Zhu</a>, <a href="/search/cs?searchtype=author&query=Hanson%2C+P+C">Paul C. Hanson</a>, <a href="/search/cs?searchtype=author&query=Xie%2C+Y">Yiqun Xie</a>, <a href="/search/cs?searchtype=author&query=Jia%2C+X">Xiaowei Jia</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="2403.18923v2-abstract-short" style="display: inline;"> Accurate prediction of dissolved oxygen (DO) concentrations in lakes requires a comprehensive study of phenological patterns across ecosystems, highlighting the need for precise selection of interactions amongst external factors and internal physical-chemical-biological variables. This paper presents the Multi-population Cognitive Evolutionary Search (MCES), a novel evolutionary algorithm for comp… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.18923v2-abstract-full').style.display = 'inline'; document.getElementById('2403.18923v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.18923v2-abstract-full" style="display: none;"> Accurate prediction of dissolved oxygen (DO) concentrations in lakes requires a comprehensive study of phenological patterns across ecosystems, highlighting the need for precise selection of interactions amongst external factors and internal physical-chemical-biological variables. This paper presents the Multi-population Cognitive Evolutionary Search (MCES), a novel evolutionary algorithm for complex feature interaction selection problems. MCES allows models within every population to evolve adaptively, selecting relevant feature interactions for different lake types and tasks. Evaluated on diverse lakes in the Midwestern USA, MCES not only consistently produces accurate predictions with few observed labels but also, through gene maps of models, reveals sophisticated phenological patterns of different lake types, embodying the innovative concept of "AI from nature, for nature". <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.18923v2-abstract-full').style.display = 'none'; document.getElementById('2403.18923v2-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> 29 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1810.02880">arXiv:1810.02880</a> <span> [<a href="https://arxiv.org/pdf/1810.02880">pdf</a>, <a href="https://arxiv.org/format/1810.02880">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="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Physics Guided Recurrent Neural Networks For Modeling Dynamical Systems: Application to Monitoring Water Temperature And Quality In Lakes </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jia%2C+X">Xiaowei Jia</a>, <a href="/search/cs?searchtype=author&query=Karpatne%2C+A">Anuj Karpatne</a>, <a href="/search/cs?searchtype=author&query=Willard%2C+J">Jared Willard</a>, <a href="/search/cs?searchtype=author&query=Steinbach%2C+M">Michael Steinbach</a>, <a href="/search/cs?searchtype=author&query=Read%2C+J">Jordan Read</a>, <a href="/search/cs?searchtype=author&query=Hanson%2C+P+C">Paul C Hanson</a>, <a href="/search/cs?searchtype=author&query=Dugan%2C+H+A">Hilary A Dugan</a>, <a href="/search/cs?searchtype=author&query=Kumar%2C+V">Vipin Kumar</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="1810.02880v1-abstract-short" style="display: inline;"> In this paper, we introduce a novel framework for combining scientific knowledge within physics-based models and recurrent neural networks to advance scientific discovery in many dynamical systems. We will first describe the use of outputs from physics-based models in learning a hybrid-physics-data model. Then, we further incorporate physical knowledge in real-world dynamical systems as additional… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1810.02880v1-abstract-full').style.display = 'inline'; document.getElementById('1810.02880v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1810.02880v1-abstract-full" style="display: none;"> In this paper, we introduce a novel framework for combining scientific knowledge within physics-based models and recurrent neural networks to advance scientific discovery in many dynamical systems. We will first describe the use of outputs from physics-based models in learning a hybrid-physics-data model. Then, we further incorporate physical knowledge in real-world dynamical systems as additional constraints for training recurrent neural networks. We will apply this approach on modeling lake temperature and quality where we take into account the physical constraints along both the depth dimension and time dimension. By using scientific knowledge to guide the construction and learning the data-driven model, we demonstrate that this method can achieve better prediction accuracy as well as scientific consistency of results. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1810.02880v1-abstract-full').style.display = 'none'; document.getElementById('1810.02880v1-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> 5 October, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2018. </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">3 pages, 3 figures, 8th International Workshop on Climate Informatics</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC 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