Data-driven Nonlinear Model Reduction to Spectral Submanifolds in Mechanical Systems

• URL: http://arxiv.org/abs/2110.01929v1
• Date: Tue, 5 Oct 2021 10:39:40 GMT
• Title: Data-driven Nonlinear Model Reduction to Spectral Submanifolds in Mechanical Systems
• Authors: Mattia Cenedese, Joar Ax{\aa}s, Haocheng Yang, Melih Eriten and George Haller
• Abstract summary: We review a data-driven nonlinear model reduction methodology based on spectral submanifolds (SSMs) As input, this approach takes observations of unforced nonlinear oscillations to construct normal forms of the dynamics reduced to very low dimensional invariants. These normal forms capture amplitude-dependent properties and are accurate enough to provide predictions for non-linearizable system response under the additions of external forcing.
• Score: 1.7499351967216341
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While data-driven model reduction techniques are well-established for linearizable mechanical systems, general approaches to reducing non-linearizable systems with multiple coexisting steady states have been unavailable. In this paper, we review such a data-driven nonlinear model reduction methodology based on spectral submanifolds (SSMs). As input, this approach takes observations of unforced nonlinear oscillations to construct normal forms of the dynamics reduced to very low dimensional invariant manifolds. These normal forms capture amplitude-dependent properties and are accurate enough to provide predictions for non-linearizable system response under the additions of external forcing. We illustrate these results on examples from structural vibrations, featuring both synthetic and experimental data.

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