Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms

Peter Benner, Pawan Goyal, Boris Kramer, Benjamin Peherstorfer, Karen Willcox

Research output: Contribution to journalArticlepeer-review

Abstract

This work presents a non-intrusive model reduction method to learn low-dimensional models of dynamical systems with non-polynomial nonlinear terms that are spatially local and that are given in analytic form. In contrast to state-of-the-art model reduction methods that are intrusive and thus require full knowledge of the governing equations and the operators of a full model of the discretized dynamical system, the proposed approach requires only the non-polynomial terms in analytic form and learns the rest of the dynamics from snapshots computed with a potentially black-box full-model solver. The proposed method learns operators for the linear and polynomially nonlinear dynamics via a least-squares problem, where the given non-polynomial terms are incorporated on the right-hand side. The least-squares problem is linear and thus can be solved efficiently in practice. The proposed method is demonstrated on three problems governed by partial differential equations, namely the diffusion–reaction Chafee–Infante model, a tubular reactor model for reactive flows, and a batch-chromatography model that describes a chemical separation process. The numerical results provide evidence that the proposed approach learns reduced models that achieve comparable accuracy as models constructed with state-of-the-art intrusive model reduction methods that require full knowledge of the governing equations.

Original languageEnglish (US)
Article number113433
JournalComputer Methods in Applied Mechanics and Engineering
Volume372
DOIs
StatePublished - Dec 1 2020

Keywords

  • Data-driven modeling
  • Model reduction
  • Nonlinear dynamical systems
  • Operator inference
  • Scientific machine learning

ASJC Scopus subject areas

  • Computational Mechanics
  • Mechanics of Materials
  • Mechanical Engineering
  • Physics and Astronomy(all)
  • Computer Science Applications

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