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Learning Topological Horseshoe via Deep Neural Networks

    https://doi.org/10.1142/S021812742430009XCited by:1 (Source: Crossref)

    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.

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