Stability preserving data-driven models with latent dynamics

Yushuang Luo, Xiantao Li, Wenrui Hao

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

In this paper, we introduce a data-driven modeling approach for dynamics problems with latent variables. The state-space of the proposed model includes artificial latent variables, in addition to observed variables that can be fitted to a given data set. We present a model framework where the stability of the coupled dynamics can be easily enforced. The model is implemented by recurrent cells and trained using backpropagation through time. Numerical examples using benchmark tests from order reduction problems demonstrate the stability of the model and the efficiency of the recurrent cell implementation. As applications, two fluid-structure interaction problems are considered to illustrate the accuracy and predictive capability of the model.

Original languageEnglish (US)
Article number081103
JournalChaos
Volume32
Issue number8
DOIs
StatePublished - Aug 1 2022

All Science Journal Classification (ASJC) codes

  • Statistical and Nonlinear Physics
  • Mathematical Physics
  • General Physics and Astronomy
  • Applied Mathematics

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