Nonlinear Reduced-Order Modeling from Data - presented by Prof. George Haller

Nonlinear Reduced-Order Modeling from Data

Prof. George Haller

Prof. George Haller
Nonlinear Reduced-Order Modeling from Data
Prof. George Haller
George Haller
ETH Zurich

I discuss a recent dynamical-systems-based alternative to machine learning in the data-driven reduced-order modeling of nonlinear phenomena. Specifically, spectral submanifolds (SSMs) represent very low-dimensional attractors in a large family of physical problems ranging from wing oscillations to transitions in pipe flows. A data-driven identification of the reduced dynamics on these SSMs gives a rigorous way to construct accurate and predictive reduced-order models for solids, fluids, and controls without the use of governing equations. I illustrate this on problems that include accelerated finite-element simulations of large structures, prediction of transitions in pipe flows, reduced-order modeling of fluid sloshing in a tank, and model-predictive control of soft robots.

References
  • 1.
    G. Haller and S. Ponsioen (2016) Nonlinear normal modes and spectral submanifolds: existence, uniqueness and use in model reduction. Nonlinear Dynamics
  • 2.
    S. Jain and G. Haller (2021) How to compute invariant manifolds and their reduced dynamics in high-dimensional finite element models. Nonlinear Dynamics
  • 3.
    J. Axås and G. Haller (2023) Model reduction for nonlinearizable dynamics via delay-embedded spectral submanifolds. Nonlinear Dynamics
Nonlinear Dynamics, an International Journal of Nonlinear Dynamics and Chaos in Engineering Systems logo
Nonlinear Dynamics
Nonlinear Dynamics, an International Journal of Nonlinear Dynamics and Chaos in Engineering Systems
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G. Haller (2024, February 22), Nonlinear Reduced-Order Modeling from Data
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Video length 38:12
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