Nonlinear Dynamics: Machine Learning
ORAL · U21 · ID: 681234
Presentations
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Applicability of Machine Learning Methodologies to Model the Statistical Evolution of the Coarse-Grained Velocity Gradient Tensor
ORAL
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Presenters
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Criston M Hyett
The University of Arizona, University of Arizona
Authors
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Criston M Hyett
The University of Arizona, University of Arizona
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Yifeng Tian
Los Alamos National Laboratory
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Michael Woodward
University of Arizona
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Michael Chertkov
University of Arizona
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Daniel Livescu
LANL, Los Alamos National Laboratory
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Mikhail Stepanov
University of Arizona, The University of Arizona
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Attention-enhanced PDE-preserved Neural Network for Predicting Spatiotemporal Physics
ORAL
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Presenters
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Xin-yang Liu
University of Notre Dame
Authors
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Xin-yang Liu
University of Notre Dame
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Jian-Xun Wang
University of Notre Dame
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Transitions in electromagnetically-driven 2D flows with random forcing configurations
ORAL
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Presenters
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Himanshi Saini
University of Minnesota
Authors
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Himanshi Saini
University of Minnesota
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Jeffrey Tithof
University of Minnesota, U Minnesota
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Manifold learning and deep autoencoders for nonlinear embedding of unsteady fluid flows
ORAL
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Presenters
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Hunor Csala
University of Utah
Authors
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Hunor Csala
University of Utah
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Scott T Dawson
Illinois Institute of Technology
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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
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Presenters
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Cristina P Martin Linares
Johns Hopkins University
Authors
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Cristina P Martin Linares
Johns Hopkins University
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Eleni Koronaki
University of Luxembourg
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Yorgos Psarellis
Johns Hopkins University
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George Karapetsas
Aristotle University of Thessaloniki
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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
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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
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Moritz Linkmann
School of Mathematic, University of Edinburgh, School of Mathematics, University of Edinburgh
Authors
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Moritz Linkmann
School of Mathematic, University of Edinburgh, School of Mathematics, University of Edinburgh
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Martin Lellep
Univ of Edinburgh, School of Physics and Astronomy, University of Edinburgh
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Jonathan Prexl
Department of Civil, Geo and Environmental Engineering, Technical University of Munich, Germany
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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
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Presenters
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Andrew J Fox
University of Wisconsin - Madison
Authors
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Andrew J Fox
University of Wisconsin - Madison
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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
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Publication: Magargal et al, AIAA Forum, DOI: 10.2514/6.2022-4169<br>Rodriguez et al, Journal of Computational Physics, DOI: 10.1016/j.jcp.2022.111141
Presenters
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Liam K Magargal
Lehigh University
Authors
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Liam K Magargal
Lehigh University
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Steven N Rodriguez
United States Naval Research Laboratory
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Justin Jaworski
Lehigh University, Lehigh
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Athanasios Iliopoulos
United States Naval Research Laboratory
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John Michopoulos
United States Naval Research Laboratory
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Predicting wake-body synchronization using deep learning
ORAL
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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
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Rajeev K Jaiman
University of British Columbia
Authors
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Amir Chizfahm
University of British Columbia
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Rajeev K Jaiman
University of British Columbia
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Spatio-Temporal Mode Decomposition for Unsteady Flow with Convolutional Neural Network
ORAL
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Presenters
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Yosuke Shimoda
Tokai University
Authors
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Yosuke Shimoda
Tokai University
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Naoya Fukushima
Tokai Univ
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Learning spatiotemporal dynamics in a turbulent flow: A 3D Autoencoded Reservoir Computer approach
ORAL
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Presenters
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Nguyen Anh Khoa Doan
Delft University of Technology
Authors
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Nguyen Anh Khoa Doan
Delft University of Technology
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Alberto Racca
Univ of Cambridge
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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
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Publication: Extrapolating fluid dynamics with spatiotemporal convolution networks (planned paper)
Presenters
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Indu Kant Deo
University of British Columbia
Authors
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Indu Kant Deo
University of British Columbia
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Rui Gao
University of British Columbia
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Rajeev K Jaiman
University of British Columbia
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