Mastering high-dimensional dynamics with Hamiltonian neural networks

• URL: http://arxiv.org/abs/2008.04214v1
• Date: Tue, 28 Jul 2020 21:14:42 GMT
• Title: Mastering high-dimensional dynamics with Hamiltonian neural networks
• Authors: Scott T. Miller, John F. Lindner, Anshul Choudhary, Sudeshna Sinha, William L. Ditto
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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