Parameter Inversion of High-Dimensional Chaotic Systems Using Neural Ordinary Differential Equations
2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS)(2024)
Abstract
High-dimensional chaotic systems possess complex structural characteristics, which have potential research value in fields like encrypted communication and chaos control. The inverse problem of parameters in high-dimensional chaotic systems is of significant importance for a profound understanding of the dynamic properties of such systems. This article takes a departure from neural ordinary differential equations and embeds the structural dynamics of the dynamical systems to the parameter inversion of high-dimensional chaotic systems. Numerical experiments on different high-dimensional chaotic systems indicate that neural ordinary differential equations perform exceptionally well in solving these inversion problems, with low errors and strong robustness.
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Key words
Neural ODEs,Data-driven,Parameter Inversion,Chaotic Systems,Deep Learning
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