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

Feb 26, 2020 (edited Apr 26, 2020)ICLR 2020 Workshop DeepDiffEq Blind SubmissionReaders: Everyone
  • Keywords: Deep Learning, Physics-induced Prior, Transparency
  • TL;DR: We propose a deep learning architecture which encodes a non-conservative physics, namely Hamiltonian dynamics with external input and energy dissipation, to improve generalization, transparency, and data efficiency.
  • 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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