Physics enhanced neural networks predict order and chaos
Computational Physics
2020-06-25 v1
Abstract
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.
Cite
@article{arxiv.1912.01958,
title = {Physics enhanced neural networks predict order and chaos},
author = {Anshul Choudhary and John F. Lindner and Elliott G. Holliday and Scott T. Miller and Sudeshna Sinha and William L. Ditto},
journal= {arXiv preprint arXiv:1912.01958},
year = {2020}
}
Comments
5 pages and 5 figures