  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.
