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Nonlinear Dynamics: Data-Driven

ORAL · Z21 · ID: 681163

Presentations

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

    ORAL

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    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

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

    ORAL

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

    ORAL

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    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. <br>[2] Li, H., Fernex, D., Semaan, R., Tan, J., Morzynski, M., & Noack, B. R. (2021). Cluster-based network model. Journal of Fluid Mechanics, 906.<br>[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.<br>[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

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    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 →