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Physics enhanced neural networks predict order and chaos

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arxiv 1912.01958 v1 pith:ODO6ZOXK submitted 2019-11-26 physics.comp-ph

Anshul Choudhary , John F. Lindner , Elliott G. Holliday , Scott T. Miller , Sudeshna Sinha , William L. Ditto This is my paper · ORCID
classification physics.comp-ph
keywords chaosnetworksneuralorderdynamicshamiltoniannonlinearpredict
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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. The power of the technique and the ubiquity of chaos suggest widespread utility.

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