Nonlinear Dynamics: Machine Learning

Presentations

• Applicability of Machine Learning Methodologies to Model the Statistical Evolution of the Coarse-Grained Velocity Gradient Tensor

  ORAL

  Nov. 22, 2022, 8:00 AM – Nov. 22, 2022, 8:13 AM

  Presenters

  • Criston M Hyett

    • The University of Arizona
    • University of Arizona

  Authors

  • Criston M Hyett

    • The University of Arizona
    • University of Arizona

  • Yifeng Tian

    • Los Alamos National Laboratory

  • Michael Woodward

    • University of Arizona

  • Michael Chertkov

    • University of Arizona

  • Daniel Livescu

    • LANL
    • Los Alamos National Laboratory

  • Mikhail Stepanov

    • University of Arizona
    • The University of Arizona

  View abstract →

• Attention-enhanced PDE-preserved Neural Network for Predicting Spatiotemporal Physics

  ORAL

  Nov. 22, 2022, 8:13 AM – Nov. 22, 2022, 8:26 AM

  Presenters

  • Xin-yang Liu

    • University of Notre Dame

  Authors

  • Xin-yang Liu

    • University of Notre Dame

  • Jian-Xun Wang

    • University of Notre Dame

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• Transitions in electromagnetically-driven 2D flows with random forcing configurations

  ORAL

  Nov. 22, 2022, 8:26 AM – Nov. 22, 2022, 8:39 AM

  Presenters

  • Himanshi Saini

    • University of Minnesota

  Authors

  • Himanshi Saini

    • University of Minnesota

  • Jeffrey Tithof

    • University of Minnesota
    • U Minnesota

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• Manifold learning and deep autoencoders for nonlinear embedding of unsteady fluid flows

  ORAL

  Nov. 22, 2022, 8:39 AM – Nov. 22, 2022, 8:52 AM

  Presenters

  • Hunor Csala

    • University of Utah

  Authors

  • Hunor Csala

    • University of Utah

  • Scott T Dawson

    • Illinois Institute of Technology

  • Amirhossein Arzani

    • University of Utah

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• From Navier-Stokes simulations for thin films to amplitude equations and back via physics-assisted machine-learning

  ORAL

  Nov. 22, 2022, 8:52 AM – Nov. 22, 2022, 9:05 AM

  Presenters

  • Cristina P Martin Linares

    • Johns Hopkins University

  Authors

  • Cristina P Martin Linares

    • Johns Hopkins University

  • Eleni Koronaki

    • University of Luxembourg

  • Yorgos Psarellis

    • Johns Hopkins University

  • George Karapetsas

    • Aristotle University of Thessaloniki

  • Ioannis G Kevrekidis

    • Johns Hopkins University

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• Interpreted machine learning in fluid dynamics: Explaining relaminarisation events in wall-bounded shear flows

  ORAL

  Nov. 22, 2022, 9:05 AM – Nov. 22, 2022, 9:18 AM

  Publication: M. Lellep, J. Prexl, B. Eckhardt, M. Linkmann, Interpreted machine learning in fluid dynamics: Explaining relaminarisation events in wall-bounded shear flows., J. Fluid Mech., 942, A2 (2022)

  Presenters

  • Moritz Linkmann

    • School of Mathematic, University of Edinburgh
    • School of Mathematics, University of Edinburgh

  Authors

  • Moritz Linkmann

    • School of Mathematic, University of Edinburgh
    • School of Mathematics, University of Edinburgh

  • Martin Lellep

    • Univ of Edinburgh
    • School of Physics and Astronomy, University of Edinburgh

  • Jonathan Prexl

    • Department of Civil, Geo and Environmental Engineering, Technical University of Munich, Germany

  • Bruno Eckhardt

    • Philipps Univ Marburg

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• Data-driven modeling of a dynamic system with extreme events through neural networks in an atlas of charts

  ORAL

  Nov. 22, 2022, 9:18 AM – Nov. 22, 2022, 9:31 AM

  Presenters

  • Andrew J Fox

    • University of Wisconsin - Madison

  Authors

  • Andrew J Fox

    • University of Wisconsin - Madison

  • Michael D Graham

    • University of Wisconsin - Madison

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• Neighbor search in latent spaces via geometric deep learning for nonlocal methods in fluid dynamics

  ORAL

  Nov. 22, 2022, 9:31 AM – Nov. 22, 2022, 9:44 AM

  Publication: Magargal et al, AIAA Forum, DOI: 10.2514/6.2022-4169
  Rodriguez et al, Journal of Computational Physics, DOI: 10.1016/j.jcp.2022.111141

  Presenters

  • Liam K Magargal

    • Lehigh University

  Authors

  • Liam K Magargal

    • Lehigh University

  • Steven N Rodriguez

    • United States Naval Research Laboratory

  • Justin Jaworski

    • Lehigh University
    • Lehigh

  • Athanasios Iliopoulos

    • United States Naval Research Laboratory

  • John Michopoulos

    • United States Naval Research Laboratory

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• Predicting wake-body synchronization using deep learning

  ORAL

  Nov. 22, 2022, 9:44 AM – Nov. 22, 2022, 9:57 AM

  Publication: Chizfahm A, Jaiman R. Deep learning for stability analysis of a freely vibrating sphere at moderate Reynolds number. arXiv preprint arXiv:2112.09858. 2021 Dec 18. (Submitted to JFM -- Under revision)

  Presenters

  • Rajeev K Jaiman

    • University of British Columbia

  Authors

  • Amir Chizfahm

    • University of British Columbia

  • Rajeev K Jaiman

    • University of British Columbia

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• Spatio-Temporal Mode Decomposition for Unsteady Flow with Convolutional Neural Network

  ORAL

  Nov. 22, 2022, 9:57 AM – Nov. 22, 2022, 10:10 AM

  Presenters

  • Yosuke Shimoda

    • Tokai University

  Authors

  • Yosuke Shimoda

    • Tokai University

  • Naoya Fukushima

    • Tokai Univ

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• Learning spatiotemporal dynamics in a turbulent flow: A 3D Autoencoded Reservoir Computer approach

  ORAL

  Nov. 22, 2022, 10:10 AM – Nov. 22, 2022, 10:23 AM

  Presenters

  • Nguyen Anh Khoa Doan

    • Delft University of Technology

  Authors

  • Nguyen Anh Khoa Doan

    • Delft University of Technology

  • Alberto Racca

    • Univ of Cambridge

  • Luca Magri

    • Imperial College London; Alan Turing Institute
    • Department of Aeronautics, Imperial College London; The Alan Turing Institute
    • Imperial College London, The Alan Turing Institute
    • Imperial College London
    • Imperial College London; The Alan Turing Institute
    • Imperial College London, Alan Turing Institute

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• Extrapolating fluid dynamics with spatiotemporal convolution networks

  ORAL

  Nov. 22, 2022, 10:23 AM – Nov. 22, 2022, 10:36 AM

  Publication: Extrapolating fluid dynamics with spatiotemporal convolution networks (planned paper)

  Presenters

  • Indu Kant Deo

    • University of British Columbia

  Authors

  • Indu Kant Deo

    • University of British Columbia

  • Rui Gao

    • University of British Columbia

  • Rajeev K Jaiman

    • University of British Columbia

  View abstract →