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arXiv · 2008.04214

Mastering high-dimensional dynamics with Hamiltonian neural networks

Scott T. Miller·John F. Lindner·Anshul Choudhary·Sudeshna Sinha·William L. Ditto
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Abstract

We detail how incorporating physics into neural network design can significantly improve the learning and forecasting of dynamical systems, even nonlinear systems of many dimensions. A map building perspective elucidates the superiority of Hamiltonian neural networks over conventional neural networks. The results clarify the critical relation between data, dimension, and neural network learning performance.

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Scott T. Miller, John F. Lindner, Anshul Choudhary, Sudeshna Sinha, William L. Ditto. 2020-07-28. Mastering high-dimensional dynamics with Hamiltonian neural networks. https://arxiv.org/abs/2008.04214

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