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Nonlinear Dynamics: Model Reduction & Turbulence III

ORAL · H31 · ID: 22881

Presentations

  • On the role of nonlinear correlations in reduced-order modeling

    ORAL

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    Publication: J. L. Callaham, S. L. Brunton, and J.-Ch. Loiseau. On the role of nonlinear correlations in reduced-order modeling (2021). https://arxiv.org/abs/2106.02409

    Presenters

    • Jared Callaham

      University of Washington

    Authors

    • Jared Callaham

      University of Washington

    • Steven L Brunton

      University of Washington, University of Washington, Seattle

    • Jean-Christophe Loiseau

      Arts et Metiers Institute of Technology, HESAM Universite

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  • Galerkin Reduced Order Models for Compressible Flows with Differentiable Programming

    ORAL

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    Presenters

    • SURYAPRATIM CHAKRABARTI

      Ohio State University

    Authors

    • SURYAPRATIM CHAKRABARTI

      Ohio State University

    • Arvind T Mohan

      Los Alamos National Laboratory, Computational Physics and Methods Group, Los Alamos National Laboratory, Los Alamos National Laboratory, Los Alamos, NM, USA

    • Daniel Livescu

      Los Alamos Natl Lab, Los Alamos National Laboratory

    • Datta V Gaitonde

      Ohio State Univ - Columbus

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  • RONS: Reduced-order nonlinear solutions for PDEs with conserved quantities

    ORAL

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    Publication: Anderson, W., & Farazmand, M. (2021). Evolution of nonlinear reduced-order solutions for PDEs with conserved quantities. In review. arXiv preprint arXiv:2104.13515.

    Presenters

    • Mohammad M Farazmand

      North Carolina State University

    Authors

    • Mohammad M Farazmand

      North Carolina State University

    • William Anderson

      North Carolina State University

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  • A new algorithm for computing global resolvent modes in a CPU and memory efficient manner

    ORAL

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    Publication: Farghadan, A., Towne, A., Martini, E., & Cavalieri, A. V. G. (2021). "A randomized time-domain algorithm for efficiently computing resolvent modes", AIAA Aviation 2021 Forum.

    Presenters

    • Ali Farghadan

      University of Michigan

    Authors

    • Ali Farghadan

      University of Michigan

    • Eduardo Martini

      Institut Pprime CNRS, Université de Poitiers ENSMA, Université de Poitiers

    • André Cavalieri

      Divisão de Engenharia Aeronáutica, Instituto Tecnológico de Aeronáutica, Instituto Tecnológico de Aeronáutica

    • Aaron S Towne

      University of Michigan

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  • Phase sensitivity analysis of post-stall airfoil wakes.

    ORAL

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    Presenters

    • Vedasri Godavarthi

      University of California, Los Angeles

    Authors

    • Vedasri Godavarthi

      University of California, Los Angeles

    • Yoji Kawamura

      Center for Mathematical Science and Advanced Technology, Japan Agency for Marine-Earth Science and Technology.

    • Kunihiko Taira

      University of California, Los Angeles, UCLA

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  • Objective discovery of fluid dynamical regimes with unsupervised machine learning

    ORAL

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    Publication: "Objective discovery of dominant dynamical processes with machine learning" is currently under review by Nature (https://www.researchsquare.com/article/rs-745356/v1) and a draft is available on arxiv (https://arxiv.org/abs/2106.12963)

    Presenters

    • Bryan Kaiser

      Los Alamos National Laboratory

    Authors

    • Bryan Kaiser

      Los Alamos National Laboratory

    • Juan A Saenz

      Los Alamos National Laboratory

    • Maike Sonnewald

      Princeton University, NOAA/OAR Geophysical Fluid Dynamics Laboratory, & the University of Washington

    • Daniel Livescu

      Los Alamos Natl Lab, Los Alamos National Laboratory

    View abstract →