LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems
Symmetry is central to modern machine learning and physics: invariances and equivariances improve sample efficiency, robustness, and out-of-distribution generalization, while symmetry principles guide scientific modeling. Yet for stochastic dynamical systems the relevant continuous symmetries are rarely known, and symm...