Identify then Realize: Contrastive Learning of Latent Port-Hamiltonian Dynamics from Partial Observations
Read the original on arXiv Machine Learning →The paper introduces CIPHER, a two‑stage identify‑then‑realize framework that learns latent port‑Hamiltonian models from partial, high‑dimensional observations. In the first stage, a contrastive teacher jointly trains an encoder and a neural ODE to produce predictive state representations. The second stage trains a student to learn an invertible coordinate transformation and port‑Hamiltonian dynamics that match encoded future observations, with theoretical guarantees for recovering the physical state up to a diffeomorphism. Experiments on ten datasets show that CIPHER matches or surpasses state‑of‑the‑art baselines, especially in long‑horizon forecasting, and remains robust to over‑specified latent dimensions while benefiting from port‑Hamiltonian parameterization.
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