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Emerging Trends in Fluid Dynamics: Physics-Informed Machine Learning and Dynamic Modeling

ORAL · R28 · ID: 1765466

Presentations

  • A Physics-Informed Machine Learning Approach for Predicting Atomized Drop Distributions in Liquid Jet Simulations

    ORAL

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    Presenters

    • Chris J Cundy

      Stanford University

    Authors

    • Chris J Cundy

      Stanford University

    • Shahab Mirjalili

      Center for Turbulence Research, Stanford University, Stanford University

    • Charlelie Laurent

      Stanford University

    • Stefano Ermon

      Stanford University

    • Ali Mani

      Stanford University, Standard University, Department of Mechanical Engineering, Stanford University

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  • Physics-Informed Neural Networks (PINN) for Enhanced Dynamic Modeling and Reverse Problem Solving in an Electro-Wetting Operated Microfluid Prism

    ORAL

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    Publication: 1 Lee, Duck-Gyu, et al.:"Dynamics of a microliquid prism actuated by electrowetting." Lab on a Chip 13.2 (2013): 274-279.

    Presenters

    • Chihoon Song

      Gachon University

    Authors

    • Chihoon Song

      Gachon University

    • Duck Gyu Lee

      Korea Institute of Machinery & Materials

    • Jeongsu LEE

      Gachon University

    • Keunhwan Park

      Gachon University

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  • DNS and physics-informed surrogate models of surfactant-laden dispersed flows

    ORAL

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    Presenters

    • Juan Pablo Valdes

      Imperial College London

    Authors

    • Juan Pablo Valdes

      Imperial College London

    • Fuyue Liang

      Imperial College London

    • Lyes Kahouadji

      Imperial College London

    • Sibo Cheng

      Imperial College London

    • Seungwon Shin

      Department of Mechanical and System Design Engineering, Hongik University, Seoul 04066, Republic of Korea, Hongik University

    • Jalel Chergui

      Université Paris Saclay, Centre National de la Recherche Scientifique (CNRS), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), 91400 Orsay, France, LISN-CNRS

    • Damir Juric

      Université Paris Saclay, Centre National de la Recherche Scientifique (CNRS), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), 91400 Orsay, France, LISN-CNRS

    • Omar K Matar

      Imperial College London

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  • Discovering self-similar blow-up solutions using physics-informed neural networks

    ORAL

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    Publication: Wang, Y., Lai, C. Y., Gómez-Serrano, J., & Buckmaster, T. (2023). Asymptotic Self-Similar Blow-Up Profile for Three-Dimensional Axisymmetric Euler Equations Using Neural Networks. Physical Review Letters, 130(24), 244002.<br><br>Wang, Y., & Lai, C. Y. (2023). Multi-stage Neural Networks: Function Approximator of Machine Precision. arXiv preprint arXiv:2307.08934.

    Presenters

    • Yongji Wang

      Stanford University

    Authors

    • Yongji Wang

      Stanford University

    • Ching-Yao Lai

      Stanford University

    • Tristan Buckmaster

      University of Maryland

    • Javier Gomez Serrano

      Brown University

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  • Cooperative swimming at low Reynolds numbers using deep reinforcement learning

    ORAL

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    Publication: Liu, Y., Zou, Z., Pak, O.S. et al. Learning to cooperate for low-Reynolds-number swimming: a model problem for gait coordination. Sci Rep 13, 9397 (2023). https://doi.org/10.1038/s41598-023-36305-y

    Presenters

    • Yangzhe Liu

      The University of Hong Kong

    Authors

    • Yangzhe Liu

      The University of Hong Kong

    • Zonghao Zou

      Cornell University

    • On Shun Pak

      Santa Clara University

    • Alan C. H. Tsang

      The University of Hong Kong

    View abstract →