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SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems
[article]
2020
arXiv
pre-print
We propose new symplectic networks (SympNets) for identifying Hamiltonian systems from data based on a composition of linear, activation and gradient modules. In particular, we define two classes of SympNets: the LA-SympNets composed of linear and activation modules, and the G-SympNets composed of gradient modules. Correspondingly, we prove two new universal approximation theorems that demonstrate that SympNets can approximate arbitrary symplectic maps based on appropriate activation functions.
arXiv:2001.03750v3
fatcat:jn2upr2r6fedxl2vvakiynul3y