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Learning Topological Horseshoe via Deep Neural Networks.
- Source :
-
International Journal of Bifurcation & Chaos in Applied Sciences & Engineering . Mar2024, Vol. 34 Issue 4, p1-11. 11p. - Publication Year :
- 2024
-
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]
Details
- Language :
- English
- ISSN :
- 02181274
- Volume :
- 34
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- International Journal of Bifurcation & Chaos in Applied Sciences & Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 176596522
- Full Text :
- https://doi.org/10.1142/S021812742430009X