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

Presentations

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

  ORAL · Invited

  Nov. 20, 2022, 4:35 PM – Nov. 20, 2022, 5:01 PM

  Publication: Herrmann, B., Baddoo, P. J., Semaan, R., Brunton, S. L. & McKeon, B. J. 'Data-driven resolvent analysis' J. Fluid Mech. 918, A10 (2021).
  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).
  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

  Nov. 20, 2022, 5:01 PM – Nov. 20, 2022, 5:27 PM

  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.
  -A. Blanchard, T. Sapsis, Bayesian optimization with output-weighted optimal sampling, Journal of Computational Physics, 425 (2021) 109901.
  -E. Pickering, G. Karniadakis, T. Sapsis, Discovering and forecasting extreme events via active learning in neural operators, ArXiv, (2022)
  -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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• New computational methods for the dynamical systems view of turbulence

  ORAL · Invited

  Nov. 20, 2022, 5:27 PM – Nov. 20, 2022, 5:53 PM

  Presenters

  • Jacob Page

    • University of Edinburgh
    • School of Mathematics, University of Edinburgh
    • Univ of Cambridge

  Authors

  • Jacob Page

    • University of Edinburgh
    • School of Mathematics, University of Edinburgh
    • Univ of Cambridge

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• Machine Learning for Scientific Discovery

  ORAL · Invited

  Nov. 20, 2022, 5:53 PM – Nov. 20, 2022, 6:19 PM

  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

  Nov. 20, 2022, 6:19 PM – Nov. 20, 2022, 6:45 PM

  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.
  [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.
  [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.
  [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.
  [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

  Nov. 20, 2022, 6:45 PM – Nov. 20, 2022, 7:11 PM

  Publication: G. Haller & S. Ponsioen, Exact model reduction by a slow-fast decomposition of nonlinear mechanical systems. Nonlinear Dynamics 90 (2017) 617-647
  
  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.
  
  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.
  
  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

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