Nonlinear Dynamics: Reduced-Order Modeling II

Presentations

• Combining probabilistic and trajectory-based learning for reduced-order modeling

  ORAL

  Nov. 21, 2022, 8:00 AM – Nov. 21, 2022, 8:13 AM

  Presenters

  • Peter J Baddoo

    • MIT

  Authors

  • Peter J Baddoo

    • MIT

  • Benjamin Herrmann

    • Universidad de Chile

  • Beverley J McKeon

    • Caltech

  • Nathan Kutz

    • University of Washington
    • University of Washington, Department of Applied Mathematics
    • UW

  • Steven L Brunton

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

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• Model Reduction for Nonlinear Systems by Balanced Truncation of State and Gradient Covariance

  ORAL

  Nov. 21, 2022, 8:13 AM – Nov. 21, 2022, 8:26 AM

  Publication: We have a manuscript in preparation.

  Presenters

  • Samuel E Otto

    • Princeton University

  Authors

  • Samuel E Otto

    • Princeton University

  • Alberto Padovan

    • Princeton University

  • Clarence W Rowley

    • Princeton

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• On-the-fly Reduced-Order Modeling of Transient Flow Response Subject to High-Dimensional External Forcing

  ORAL

  Nov. 21, 2022, 8:26 AM – Nov. 21, 2022, 8:39 AM

  Presenters

  • Alireza Amiri-Margavi

    • University of Pittsburgh

  Authors

  • Alireza Amiri-Margavi

    • University of Pittsburgh

  • Hessam Babaee

    • University of Pittsburgh

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• A fast method for computing nonlinear reduced-order models with conserved quantities

  ORAL

  Nov. 21, 2022, 8:39 AM – Nov. 21, 2022, 8:52 AM

  Presenters

  • Mohammad M Farazmand

    • North Carolina State University

  Authors

  • Mohammad M Farazmand

    • North Carolina State University

  • William Anderson

    • North Carolina State University

  • Zack Hilliard

    • NC State University

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• Data-driven reduced-order models using quadratic manifolds

  ORAL

  Nov. 21, 2022, 8:52 AM – Nov. 21, 2022, 9:05 AM

  Presenters

  • Aniketh Kalur

    • University of Minnesota

  Authors

  • Aniketh Kalur

    • University of Minnesota

  • Rudy Geelen

    • University of Texas at Austin

  • Karen E Willcox

    • University of Texas at Austin

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• Nonlinear Oblique Projections for Reduced-Order Modeling using Constrained Autoencoders

  ORAL

  Nov. 21, 2022, 9:05 AM – Nov. 21, 2022, 9:18 AM

  Presenters

  • Gregory R Macchio

    • Princeton University

  Authors

  • Gregory R Macchio

    • Princeton University

  • Samuel E Otto

    • Princeton University

  • Clarence W Rowley

    • Princeton

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• SPOD-based Spectral Galerkin Reduced Order Models

  ORAL

  Nov. 21, 2022, 9:18 AM – Nov. 21, 2022, 9:31 AM

  Presenters

  • Cong Lin

    • University of California San Diego

  Authors

  • Cong Lin

    • University of California San Diego

  • Oliver T. Schmidt

    • University of California San Diego

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• On-the-fly sparse interpolation for stochastic reduced-order modeling with time-dependent bases

  ORAL

  Nov. 21, 2022, 9:31 AM – Nov. 21, 2022, 9:44 AM

  Presenters

  • Mohammad Hossein Naderi

    • University of Pittsburgh

  Authors

  • Mohammad Hossein Naderi

    • University of Pittsburgh

  • Hessam Babaee

    • University of Pittsburgh

  • Mohammad Hossein Naderi

    • University of Pittsburgh

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• Data-driven mean-field modeling for complex wake dynamics -- Towards automatable data-driven ROM

  ORAL

  Nov. 21, 2022, 9:44 AM – Nov. 21, 2022, 9:57 AM

  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.
  [2] Deng, N., Noack, B. R., Morzyński, M. & Pastur, L. R. 2021 Galerkin force model for transient and post-transient dynamics of the fluidic pinball. J. Fluid Mech., 918, A4.
  [3] Deng, N., Noack, B. R., Morzyński, M. & Pastur, L. R. 2022 Cluster-based hierarchical network model of the fluidic pinball -- cartographing transient and post-transient, multi-frequency, multi-attractor behaviour. J. Fluid Mech. 934 A24.
  [4] Brunton, S. L., Proctor, J. L. & Kutz, J. N. 2016 Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proc. Natl Acad. Sci. USA 113 (5), 3932–3937.
  [5] Albert, R. & Barabási, A.-L. 2002 Statistical mechanics of complex networks. Rev. Mod. Phys. 74 (1), 47.

  Presenters

  • Nan DENG

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

  Authors

  • Nan DENG

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

  • Luc Pastur

    • IMSIA, ENSTA Paris, Institut polytechnique de Paris

  • Marek Morzynski

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

  • Bernd R Noack

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

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• Time-Series Velocity Field Reconstruction Based on Unsteady Pressure Sensors for Feedback Control of Separated Flow around an Airfoil

  ORAL

  Nov. 21, 2022, 9:57 AM – Nov. 21, 2022, 10:10 AM

  Presenters

  • Yoshiki Anzai

    • Tohoku University

  Authors

  • Yoshiki Anzai

    • Tohoku University

  • Shintaro Goto

    • Tohoku University

  • Yasuo Sasaki

    • Tohoku University

  • Kumi Nakai

    • Tohoku University

  • Atsushi Komuro

    • The University of Tokyo

  • Taku Nonomura

    • Tohoku Univ
    • Tohoku University

  View abstract →