Identify then Realize: Contrastive Learning of Latent Port-Hamiltonian Dynamics from Partial Observations

Identifying latent state representations and dynamics is essential when direct modeling in observation space is infeasible, particularly under partial and high-dimensional observations. In such settings, representation learning and physics-aware modeling are inherently coupled. We propose CIPHER, a