Title

Data-Driven Nonlinear Modal Analysis: A Deep Learning Approach

Document Type

Conference Proceeding

Publication Date

7-29-2022

Department

Department of Mechanical Engineering-Engineering Mechanics

Abstract

We present a data-driven method based on deep learning for identifying nonlinear normal modes of unknown nonlinear dynamical systems using response data only. We leverage the modeling capacity of deep neural networks to identify the forward and inverse nonlinear modal transformations and the associated modal dynamics evolution. We test the method on Duffing systems with cubic nonlinearity and observe that the identified NNMs with invariant manifolds from response data agree with those analytical or numerical ones using closed-form equations.

Publication Title

Conference Proceedings of the Society for Experimental Mechanics Series

ISBN

9783031040856

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