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1. Learning Topological Horseshoe via Deep Neural Networks.

Author

Yang, Xiao-Song and Cheng, Junfeng

Subjects
*ARTIFICIAL neural networks, *HORSESHOES, *RUNGE-Kutta formulas, *DYNAMICAL systems, *NONLINEAR systems
Abstract

Deep Neural Networks (DNNs) have been successfully applied to investigations of numerical dynamics of finite-dimensional nonlinear systems such as ODEs instead of finding numerical solutions to ODEs via the traditional Runge–Kutta method and its variants. To show the advantages of DNNs, in this paper, we demonstrate that the DNNs are more efficient in finding topological horseshoes in chaotic dynamical systems. [ABSTRACT FROM AUTHOR]

Published
2024
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