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 two-stage identify-then-realize framework for learning latent port-Hamiltonian models of conservative and dissipative systems. First, a contrastive teacher jointly learns an encoder and a neural ODE to obtain predictive state representations from observation histories. Second, a student learns an invertible nonlinear coordinate transformation and port-Hamiltonian dynamics by matching encoded ob...
| Authors | Peilun Li, Kaiyuan Tan, Daniel Moyer et al. |
|---|---|
| Primary category | cs.LG |
| Published | 15 May 2026 |
| Last updated at source | 1 October 2026 |
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