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Minisymposia: Reduced-Order Modeling in Fluids Via Artificial and Human Intelligence

INVITED · J01 · ID: 683114

Presentations

  • Some aspects of combined equation- and data-driven modeling for turbulent flows

    ORAL · Invited

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    Publication: Herrmann, B., Baddoo, P. J., Semaan, R., Brunton, S. L. & McKeon, B. J. 'Data-driven resolvent analysis' J. Fluid Mech. 918, A10 (2021).<br>Baddoo, P. J., Herrmann, B., McKeon, B. J. & Brunton, S. L. 'Kernel learning for robust dynamic mode decomposition: Linear And Nonlinear Disambiguation Optimization (LANDO)' Proc. A Royal Soc., 478, 20210830 (2022).<br>McKeon, B. J. & Sharma, A. 'A critical layer framework for turbulent pipe flow' J. Fluid Mech., 658, 336-382 (2010); also ArXiV 1001.3100 (2009).

    Presenters

    • Beverley J McKeon

      Caltech

    Authors

    • Beverley J McKeon

      Caltech

    • Benjamin Herrmann

      Universidad de Chile

    • Peter J Baddoo

      MIT

    • Steven L Brunton

      University of Washington, University of Washington, Department of Mechanical Engineering

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  • Likelihood-weighted active learning with application to Bayesian optimization and uncertainty quantification for complex fluid flows

    ORAL · Invited

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    Publication: -T. Sapsis, A. Blanchard, Optimal criteria and their asymptotic form for data selection in data-driven reduced-order modeling with Gaussian process regression, Philosophical Transactions of the Royal Society A, 380 (2022) 20210197.<br>-A. Blanchard, T. Sapsis, Bayesian optimization with output-weighted optimal sampling, Journal of Computational Physics, 425 (2021) 109901.<br>-E. Pickering, G. Karniadakis, T. Sapsis, Discovering and forecasting extreme events via active learning in neural operators, ArXiv, (2022)<br>-A. Blanchard, G. C. Maceda, D. Fan, Y. Li, Y. Zhou, B. Noack, T. Sapsis, Bayesian optimization for active flow control, Acta Mechanica Sinica, 37 (2021) 1786-1798.

    Presenters

    • Themistoklis Sapsis

      Massachusetts Institute of Technology MIT, Massachusetts Institute of Technology MI

    Authors

    • Themistoklis Sapsis

      Massachusetts Institute of Technology MIT, Massachusetts Institute of Technology MI

    • Antoine Blanchard

      Massachusetts Institute of Technology MIT

    • Ethan M Pickering

      Massachusetts Institute of Technology

    • Stephen Guth

      Massachusetts Institute of Technology

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  • Machine Learning for Scientific Discovery

    ORAL · Invited

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    Presenters

    • Steven L Brunton

      University of Washington, University of Washington, Department of Mechanical Engineering

    Authors

    • Steven L Brunton

      University of Washington, University of Washington, Department of Mechanical Engineering

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  • Modes, Manifolds and Clusters—Different flavours of reduced-order models

    ORAL · Invited

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    Publication: [1] Deng, N., Noack, B. R., Morzyński, M., & Pastur, L. R. 2020 Low-order model for successive bifurcations of the fluidic pinball. J. Fluid Mech. 884, A37:1–41.<br>[2] Deng, N., Noack, B. R., Morzyński, M., & Pastur, L. R. 2021 Galerkin force model for transient dynamics of the fluidic pinball. J. Fluid Mech. 918, A04:1–37.<br>[3] Deng, N., Noack, B. R., Morzyński, M. & Pastur, L. 2022 Cluster-based hierarchical network model of the fluidic pinball—Cartographing transient and post-transient, multi-frequency, multi-attractor behaviour. J. Fluid Mech. 934, article A24: 1–44.<br>[4] Loiseau, J.-Ch., Noack, B. R. & Brunton, S. L. 2018 Sparse reduced-order modeling: Sensor-based dynamics to full-state estimation. J. Fluid Mech. 844, 459-490.<br>[5] Fernex, D., Noack, B. R. & Semaan, R. 2021 Cluster-based network modeling—From snapshots to complex dynamical systems. Science Advances 7(25), eabf5006:1. . .10.

    Presenters

    • Bernd R Noack

      Harbin Institute of Technology, Shenzhen, P.R. China

    Authors

    • Bernd R Noack

      Harbin Institute of Technology, Shenzhen, P.R. China

    • Nan Deng

      Harbin Institute of Technology, Shenzhen, P.R. China

    • Chang Hou

      Harbin Institute of Technology, Shenzhen, P.R. China

    • Luc Pastur

      ENSTA, Paris, France

    • Marek Morzynski

      Poznan University of Technology, Poland, Department of Virtual Engineering, Poznań University of Technology, PL 60-965 Poznań, Poland

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  • Data-driven Flow Models from Nonlinear Spectral Reduction

    ORAL · Invited

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    Publication: G. Haller & S. Ponsioen, Exact model reduction by a slow-fast decomposition of nonlinear mechanical systems. Nonlinear Dynamics 90 (2017) 617-647<br><br>M. Cenedese, J. Axås, B. Bäuerlein, K. Avila & G. Haller, Data-driven modeling and prediction of non-linearizable dynamics via spectral submanifolds Nat. Commun. 13 (2022) 872.<br><br>G. Haller, S. Jain & M. Cenedese, Dynamics-based Machine Learning for Nonlinearizable Phenomena: Data-driven Reduced Models on Spectral Submanifolds. SIAM News, 55/5 (2022) 1-4.<br><br>B. Kaszás, M. Cenedese & G. Haller Dynamics-based machine learning of transitions in Couette flow. arXiv:2203.13098 (2022)

    Presenters

    • George Haller

      ETH Zurich

    Authors

    • George Haller

      ETH Zurich

    View abstract →