Machine-learning parameter tracking with partial state observation
Zheng-Meng ZhaiMohammadamin MoradiBryan GlazMulugeta Haileand Ying-Cheng Lai
Zheng-Meng ZhaiMohammadamin MoradiBryan GlazMulugeta Haileand Ying-Cheng Lai
Nov 2023
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摘要原文
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.
