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

Presentations

• Deterministic local reduced-order modelling for chaotic flows with cluster-based quantization

  ORAL

  Nov. 25, 2024, 4:45 PM – Nov. 25, 2024, 4:58 PM

  Presenters

  • Antonio Colanera

    • Politecnico di Torino

  Authors

  • Antonio Colanera

    • Politecnico di Torino

  • 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 →

• Intrinsic Instabilities and Generalization Challenges in Neural Partial Differential Equations

  ORAL

  Nov. 25, 2024, 4:58 PM – Nov. 25, 2024, 5:11 PM

  Presenters

  • Arvind T Mohan

    • Los Alamos National Laboratory (LANL)

  Authors

  • Arvind T Mohan

    • Los Alamos National Laboratory (LANL)

  • Ashesh K Chattopadhyay

    • University of California, Santa Cruz

  • Jonah M Miller

    • Los Alamos National Laboratory

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• Submerged Reduced-Order Models for Incompressible Flow around Obstacles

  ORAL

  Nov. 25, 2024, 5:11 PM – Nov. 25, 2024, 5:24 PM

  Publication: We plan to submit a manuscript derived from this work to publish in Computer Methods in Applied Mechanics and Engineering, but are still in the process of preparing the manuscript

  Presenters

  • Jacob W Murri

    • University of California, Los Angeles

  Authors

  • Jacob W Murri

    • University of California, Los Angeles

  • Clifford E Watkins

    • Special Technologies Laboratory (STL)
    • Nevada National Security Sites

  • James Watts

    • Colorado School of Mines

  • Sean R Breckling

    • Nevada National Security Site (NNSS)

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• Data-driven artificial viscosity closures for projection-based reduced order modeling of incompressible fluid flows

  ORAL

  Nov. 25, 2024, 5:24 PM – Nov. 25, 2024, 5:37 PM

  Publication: Aviral Prakash and Yongjie Jessica Zhang, Projection-based reduced order modeling and data-driven artificial viscosity closures for incompressible fluid flows, Computer Methods in Applied Mechanics and Engineering, Volume 425, 2024

  Presenters

  • Aviral Prakash

    • Carnegie Mellon University

  Authors

  • Aviral Prakash

    • Carnegie Mellon University

  • Yongjie J Zhang

    • Carnegie Mellon University

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• Flexi-Propagator For Partial Differential Equations

  ORAL

  Nov. 25, 2024, 5:37 PM – Nov. 25, 2024, 5:50 PM

  Presenters

  • Khalid Rafiq

    • University of Nevada, Reno

  Authors

  • Khalid Rafiq

    • University of Nevada, Reno

  • Wenjing Liao

    • Georgia Institute of Technology, Atlanta

  • Aditya G Nair

    • University of Nevada, Reno

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• Bundle embeddings for learning chaotic dynamics from irregularly sampled partial observable data

  ORAL

  Nov. 25, 2024, 5:50 PM – Nov. 25, 2024, 6:03 PM

  Presenters

  • Charles Douglas Young

    • Los Alamos National Laboratory (LANL)

  Authors

  • Charles Douglas Young

    • Los Alamos National Laboratory (LANL)

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• Objective Determination of Optimal POD Modes for Large-Scale Motion Reconstruction in Turbulent Flows

  ORAL

  Nov. 25, 2024, 6:03 PM – Nov. 25, 2024, 6:16 PM

  Presenters

  • Nathan Ziems

    • University of Indianapolis

  Authors

  • Nathan Ziems

    • University of Indianapolis

  • Venkatesh Pulletikurthi

    • Purdue University
    • Friedrich-Alexander-Universität Erlangen-Nürnberg

  • Suranga I Dharmarathne

    • University of Indianapolis

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• Continuous latent flow modeling for model-based reinforcement learning using temporal transformer networks

  ORAL

  Nov. 25, 2024, 6:16 PM – Nov. 25, 2024, 6:29 PM

  Presenters

  • Christian Lagemann

    • AI Institute in Dynamic Systems, University of Washington
    • University of Washington

  Authors

  • Christian Lagemann

    • AI Institute in Dynamic Systems, University of Washington
    • University of Washington

  • Kai Lagemann

    • Statistics and Machine Learning, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany

  • Steven L Brunton

    • University of Washington

  View abstract →