Machine-learning parameter tracking with partial state observation

Zheng Meng Zhai, Mohammadamin Moradi, Bryan Glaz, Mulugeta Haile, Ying Cheng Lai

Research output: Contribution to journalArticlepeer-review

Abstract

Complex and nonlinear dynamical systems often involve parameters that change with time, accurate tracking of which is essential to tasks such as state estimation, prediction, and control. Existing machine-learning methods require full state observation of the underlying system and tacitly assume adiabatic changes in the parameter. Formulating an inverse problem and exploiting reservoir computing, we develop a model-free and fully data-driven framework to accurately track time-varying parameters from partial state observation in real time. In particular, with training data from a subset of the dynamical variables of the system for a small number of known parameter values, the framework is able to accurately predict the parameter variations in time. Low- and high-dimensional, Markovian and non-Markovian, and spatiotemporal nonlinear dynamical systems are used to demonstrate the power of the machine-learning based parameter-tracking framework. Pertinent issues affecting the tracking performance are addressed.

Original languageEnglish (US)
Article number013196
JournalPhysical Review Research
Volume6
Issue number1
DOIs
StatePublished - Jan 2024
Externally publishedYes

ASJC Scopus subject areas

  • General Physics and Astronomy

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