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

• URL: http://arxiv.org/abs/2002.08860v3
• Date: Thu, 30 Apr 2020 02:53:24 GMT
• Title: Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning
• Authors: Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty
• Abstract summary: We introduce Dissipative SymODEN, a deep learning architecture which can infer the dynamics of a physical system with dissipation from observed state trajectories. Dissipative SymODEN encodes the port-Hamiltonian dynamics with energy dissipation and external input into the design of its graph and learns the dynamics in a structured way.
• Score: 9.811643357656196
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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