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Physics enhanced neural networks predict order and chaos
[article]
2019
arXiv
pre-print
Preserved Fulltext
Conventional artificial neural networks are powerful tools in science and industry, but they can fail when applied to nonlinear systems where order and chaos coexist. We use neural networks that incorporate the structures and symmetries of Hamiltonian dynamics to predict phase space trajectories even as nonlinear systems transition from order to chaos. We demonstrate Hamiltonian neural networks on the canonical Henon-Heiles system, which models diverse dynamics from astrophysics to chemistry.
arXiv:1912.01958v1
fatcat:7fhlnwkbrzgcpdquxfomrwdlxa