Discovering Phase Space Structure in Learned Hamiltonian Systems
arXiv:2509.23003v3 Announce Type: replace-cross Abstract: Mechanics-inspired neural dynamical models improve interpretability and physical consistency, but they usually assume a known configuration space, fixed physical parameters, and deterministic prediction from a prescribed initial condition. We introduce Structured Phase Space GAN (SPS-GAN), a conditional generative model that learns structured dynamics from both Cartesian trajectories and video. SPS-GAN combines a port-Hamiltonian backbon…