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Learning physics-based reduced-order models from data using nonlinear manifolds.
Geelen, Rudy; Balzano, Laura; Wright, Stephen; Willcox, Karen.
Afiliação
  • Geelen R; Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas 78712, USA.
  • Balzano L; Electrical Engineering and Computer Science Department, University of Michigan, Ann Arbor, Michigan 48109, USA.
  • Wright S; Computer Sciences Department, University of Wisconsin, Madison, Wisconsin 53706, USA.
  • Willcox K; Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas 78712, USA.
Chaos ; 34(3)2024 Mar 01.
Article em En | MEDLINE | ID: mdl-38470262
ABSTRACT
We present a novel method for learning reduced-order models of dynamical systems using nonlinear manifolds. First, we learn the manifold by identifying nonlinear structure in the data through a general representation learning problem. The proposed approach is driven by embeddings of low-order polynomial form. A projection onto the nonlinear manifold reveals the algebraic structure of the reduced-space system that governs the problem of interest. The matrix operators of the reduced-order model are then inferred from the data using operator inference. Numerical experiments on a number of nonlinear problems demonstrate the generalizability of the methodology and the increase in accuracy that can be obtained over reduced-order modeling methods that employ a linear subspace approximation.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article