[2204.11744] Stability Preserving Data-driven Models With Latent Dynamics
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Mathematics > Optimization and Control

arXiv:2204.11744 (math)
[Submitted on 20 Apr 2022]

Title:Stability Preserving Data-driven Models With Latent Dynamics

Authors:Yushuang Luo, Xiantao Li, Wenrui Hao
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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.
Subjects: Optimization and Control (math.OC); Machine Learning (cs.LG); Numerical Analysis (math.NA); Computational Physics (physics.comp-ph)
Cite as: arXiv:2204.11744 [math.OC]
  (or arXiv:2204.11744v1 [math.OC] for this version)
  https://doi.org/10.48550/arXiv.2204.11744
Related DOI: https://doi.org/10.1063/5.0096889

Submission history

From: Xiantao Li [view email]
[v1] Wed, 20 Apr 2022 00:41:10 UTC (783 KB)
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