Low-Order Modeling and Machine Learning in Fluid Dynamics: Methods IV

Presentations

• A transformer-based model for grid-agnostic full-field reconstruction of tsunami waves from sparse observations.

  ORAL

  Nov. 25, 2024, 3:50 PM – Nov. 25, 2024, 4:03 PM

  Presenters

  • Edward McDugald

    Los Alamos National Laboratory, University of Arizona

  Authors

  • Edward McDugald

    Los Alamos National Laboratory, University of Arizona

  • Arvind T Mohan

    Los Alamos National Laboratory (LANL)

  • Darren Engwirda

    Los Alamos National Laboratory

  • Javier Santos

    Los Alamos National Laboratory

  • Agnese Marcato

    Los Alamos National Lab

  View abstract →

• Deep Reduced-Order Modeling for Fluids: The Importance of Autoencoder Initialization and Initial Transient Data

  ORAL

  Nov. 25, 2024, 4:03 PM – Nov. 25, 2024, 4:16 PM

  Presenters

  • Gregory Robert Macchio

    Princeton University

  Authors

  • Gregory Robert Macchio

    Princeton University

  • Clarence W Rowley

    Princeton

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• Feature-Guided Adaptive Model Order Reduction for Convection-Dominated Problems

  ORAL

  Nov. 25, 2024, 4:16 PM – Nov. 25, 2024, 4:29 PM

  Presenters

  • Ali Mohaghegh

    University of Kansas

  Authors

  • Ali Mohaghegh

    University of Kansas

  • Cheng Huang

    University of Kansas

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• Linear and nonlinear Granger causality analysis of turbulent flows

  ORAL

  Nov. 25, 2024, 4:29 PM – Nov. 25, 2024, 4:42 PM

  Publication: Lopez-Doriga, Barbara & Atzori, Marco & Vinuesa, Ricardo & Bae, H. Jane & Srivastava, Ankit & Dawson, Scott. (2024). Linear and nonlinear Granger causality analysis of turbulent duct flows. Journal of Physics: Conference Series. 2753. 012017. 10.1088/1742-6596/2753/1/012017.

  Presenters

  • Barbara Lopez-Doriga

    University of California, Los Angeles

  Authors

  • Barbara Lopez-Doriga

    University of California, Los Angeles

  • Marco Atzori

    Politecnico di Milano

  • Ricardo Vinuesa

    KTH Royal Institute of Technology

  • Jane Bae

    Caltech, California Institute of Technology

  • Ankit Srivastava

    Illinois Institute of Technology

  • Scott T. M. Dawson

    Illinois Institute of Technology

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• Extracting self-similarity from data

  ORAL

  Nov. 25, 2024, 4:42 PM – Nov. 25, 2024, 4:55 PM

  Publication: arXiv:2407.10724

  Presenters

  • Kostas Steiros

    Imperial College London

  Authors

  • Nikolaos Bempedelis

    Queen Mary University of London

  • Luca Magri

    Imperial College London, The Alan Turing Institute, PoliTo, Imperial College London, Alan Turing Institute, Politecnico di Torino, Imperial College London, Alan Turing Institute

  • Kostas Steiros

    Imperial College London

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• Neural Inference of Fluid–Structure Interactions from Lagrangian Particle Tracks

  ORAL

  Nov. 25, 2024, 4:55 PM – Nov. 25, 2024, 5:08 PM

  Presenters

  • Rui Tang

    The Pennsylvania State University

  Authors

  • Rui Tang

    The Pennsylvania State University

  • Ke Zhou

    Pennsylvania State University

  • Samuel J Grauer

    Pennsylvania State University

  • jifu tan

    Northern Illinois University

  View abstract →

• Physics-Constrained Forecasting of Fluid Dynamics with Reservoir Computing

  ORAL

  Nov. 25, 2024, 5:08 PM – Nov. 25, 2024, 5:21 PM

  Presenters

  • Dima Tretiak

    University of Washington

  Authors

  • Dima Tretiak

    University of Washington

  • Anastasia Bizyaeva

    Cornell University

  • J. Nathan Kutz

    University of Washington, University of Washington, AI Institute for Dynamic Systems

  • Steven L Brunton

    University of Washington

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• Combined autoencoder and clustering-based approach to investigate extreme events in turbulent flows

  ORAL

  Nov. 25, 2024, 5:21 PM – Nov. 25, 2024, 5:34 PM

  Presenters

  • Nguyen Anh Khoa Doan

    Delft University of Technology

  Authors

  • Nguyen Anh Khoa Doan

    Delft University of Technology

  • Luca Magri

    Imperial College London, The Alan Turing Institute, PoliTo, Imperial College London, Alan Turing Institute, Politecnico di Torino, Imperial College London, Alan Turing Institute

  View abstract →