Machine-learning parameter tracking with partial state observation

• URL: http://arxiv.org/abs/2311.09142v1
• Date: Wed, 15 Nov 2023 17:39:25 GMT
• Title: Machine-learning parameter tracking with partial state observation
• Authors: Zheng-Meng Zhai, Mohammadamin Moradi, Bryan Glaz, Mulugeta Haile, and Ying-Cheng Lai
• Abstract summary: 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. We develop a model-free and fully data-driven framework to accurately track time-varying parameters from partial state observation in real time. Low- and high-dimensional, Markovian and non-Markovian nonlinear dynamical systems are used to demonstrate the power of the machine-learning based parameter-tracking framework.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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