Nonlinear Dynamics: Data-Driven

Presentations

• Why are the data-driven surrogates of multi-scale dynamical systems long-term unstable?

  ORAL

  Nov. 22, 2022, 12:50 PM – Nov. 22, 2022, 1:03 PM

  Presenters

  • Ashesh K Chattopadhyay

    Rice University

  Authors

  • Ashesh K Chattopadhyay

    Rice University

  • Ebrahim Nabizadeh

    Rice University

  • Pedram Hassanzadeh

    Rice, Rice University

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• Calibration of projection-based compressible flow reduced order models with quadratic manifold approximation

  ORAL

  Nov. 22, 2022, 1:03 PM – Nov. 22, 2022, 1:16 PM

  Presenters

  • Victor Zucatti

    University of Notre Dame

  Authors

  • Victor Zucatti

    University of Notre Dame

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• Modeling low-frequency dynamics in turbulent flows using data

  ORAL

  Nov. 22, 2022, 1:16 PM – Nov. 22, 2022, 1:29 PM

  Presenters

  • Sijie Huang

    Arizona State University

  Authors

  • Sijie Huang

    Arizona State University

  • Jeonglae Kim

    Arizona State University

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• Data-driven discovery and extrapolation of parameterized pattern-forming dynamics

  ORAL

  Nov. 22, 2022, 1:29 PM – Nov. 22, 2022, 1:42 PM

  Publication: Data-driven discovery and extrapolation of parameterized pattern-forming dynamics, in preparation

  Presenters

  • Zachary G Nicolaou

    University of Washington

  Authors

  • Zachary G Nicolaou

    University of Washington

  • Steven L Brunton

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

  • Nathan Kutz

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

  • Guanyu Huo

    University of Washington

  • Yihui Chen

    University of Washington

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• Data-driven prediction of Jacobians and Covariant Lyapunov Vectors in chaotic flows

  ORAL

  Nov. 22, 2022, 1:42 PM – Nov. 22, 2022, 1:55 PM

  Publication: Planned paper: "Data-driven prediction of Jacobians and Covariant Lyapunov Vectors in chaotic flows", Georgios Margazoglou and Luca Magri, (in preparation, 2022).

  Presenters

  • Georgios Margazoglou

    Imperial College London

  Authors

  • Georgios Margazoglou

    Imperial College London

  • Luca Magri

    Imperial College London; Alan Turing Institute, Department of Aeronautics, Imperial College London; The Alan Turing Institute, Imperial College London, The Alan Turing Institute, Imperial College London, Imperial College London; The Alan Turing Institute, Imperial College London, Alan Turing Institute

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• Constructing invariant solutions of wall-bounded shear flows by a Jacobian-free adjoint-based method

  ORAL

  Nov. 22, 2022, 1:55 PM – Nov. 22, 2022, 2:08 PM

  Presenters

  • Omid Ashtari

    Ecole Polytechnique Federale de Lausanne

  Authors

  • Omid Ashtari

    Ecole Polytechnique Federale de Lausanne

  • Tobias M Schneider

    Ecole Polytechnique Federale de Lausanne

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• Discovery of interpretable structural model errors by combining Bayesian sparse regression and data-assimilation

  ORAL

  Nov. 22, 2022, 2:08 PM – Nov. 22, 2022, 2:21 PM

  Publication: Rambod Mojgani, Ashesh Chattopadhyay, and Pedram Hassanzadeh , "Discovery of interpretable structural model errors by combining Bayesian sparse regression and data assimilation: A chaotic Kuramoto–Sivashinsky test case", Chaos 32, 061105 (2022) https://doi.org/10.1063/5.0091282

  Presenters

  • Rambod Mojgani

    Rice University

  Authors

  • Rambod Mojgani

    Rice University

  • Ashesh K Chattopadhyay

    Rice University

  • Pedram Hassanzadeh

    Rice, Rice University

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• Optimal Sparse Sensor Placement with Adaptive Constraints for Nuclear Digital Twins

  ORAL

  Nov. 22, 2022, 2:21 PM – Nov. 22, 2022, 2:34 PM

  Presenters

  • Niharika Karnik

    University of Washington

  Authors

  • Niharika Karnik

    University of Washington

  • Mohammad G Abdo

    Idaho National Laboratory

  • Krithika Manohar

    University of Washington

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• Trajectory-optimized cluster-based network model for the three-dimensional sphere wake

  ORAL

  Nov. 22, 2022, 2:34 PM – Nov. 22, 2022, 2:47 PM

  Publication: [1] Fernex, D., Noack, B. R., & Semaan, R. (2021). Cluster-based network modeling–From snapshots to complex dynamical systems. Science Advances, 7(25), eabf5006.
  [2] Li, H., Fernex, D., Semaan, R., Tan, J., Morzynski, M., & Noack, B. R. (2021). Cluster-based network model. Journal of Fluid Mechanics, 906.
  [3] Deng, N., Noack, B. R., Morzynski, M., & Pastur, L. R. (2022). Cluster-based hierarchical network model of the fluidic pinball–cartographing transient and post-transient, multi-frequency, multi-attractor behaviour. Journal of Fluid Mechanics, 934.
  [4] Hou, C., Deng, N., Noack, B. R. (2022). Trajectory-optimized cluster-based network model for the sphere wake. Physics of Fluids, (in print, DOI: 10.1063/5.0098655).

  Presenters

  • Chang Hou

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

  Authors

  • Chang Hou

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

  • Nan DENG

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

  • Bernd R Noack

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

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• Dynamics-based machine learning of transitions in shear flows

  ORAL

  Nov. 22, 2022, 2:47 PM – Nov. 22, 2022, 3:00 PM

  Publication: B. Kaszás, M. Cenedese & G. Haller Dynamics-based machine learning of transitions in Couette flow arXiv:2203.13098 (2022).

  Presenters

  • Balint Kaszas

    ETH Zurich

  Authors

  • Balint Kaszas

    ETH Zurich

  • Mattia Cenedese

    ETH Zurich

  • George Haller

    ETH Zurich

  View abstract →