Self-Supervised Learning of Task-Agnostic Representations for Physical Dynamics

Foundation models for spatio-temporal physical systems seek representations that generalize across systems, parameters, and operating conditions. We explore a self-supervised route to this goal, decoupling representation learning from task-specific prediction to capture reusable properties of physical dynamics. We study masked reconstruction in physical space and JEPA-style prediction in latent space, and introduce regime fingerprinting to capture trajectory-level properties of the dynamics: an early partial observation must identify a broader view of the same trajectory, thereby encouraging the preservation of information that persists across time. The learned representations provide explicit, compact conditioning signals for downstream models and support efficient in-context adaptation to previously unseen physical systems. Experiments on heterogeneous 2D and 3D spatio-temporal dynamics across multiple downstream tasks show strong performance in few-shot settings, together with computationally efficient cross-physics generalization.