Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning
Machine Learning
2020-05-01 v3 Systems and Control
Systems and Control
Machine Learning
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.
Cite
@article{arxiv.2002.08860,
title = {Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning},
author = {Yaofeng Desmond Zhong and Biswadip Dey and Amit Chakraborty},
journal= {arXiv preprint arXiv:2002.08860},
year = {2020}
}
Comments
Published at ICLR 2020 Workshop on Integration of Deep Neural Models and Differential Equations (DeepDiffEq)