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arXiv · 2002.08860

Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning

Yaofeng Desmond Zhong·Biswadip Dey·Amit Chakraborty
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Abstract

In this work, we introduce Dissipative SymODEN, a deep learning architecture which can infer the dynamics of a physical system with dissipation from observed state trajectories. To improve prediction accuracy while reducing network size, Dissipative SymODEN encodes the port-Hamiltonian dynamics with energy dissipation and external input into the design of its computation graph and learns the dynamics in a structured way. The learned model, by revealing key aspects of the system, such as the inertia, dissipation, and potential energy, paves the way for energy-based controllers.

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BibTeXRIS

Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty. 2020-02-20. Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning. https://arxiv.org/abs/2002.08860

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