CaLiSym: Learning Symplectic Dynamics of Real-World Systems through Structured Canonical Lifts

arXiv:2607.06824v1 Announce Type: new Abstract: Physics-informed learning promises data-efficient and stable dynamics prediction, yet its strongest geometric guarantees have largely remained confined to closed conservative systems. This excludes many robotic systems of practical interest, where actuation, dissipation, and constraints continuously exchange energy and momentum with the environment. We introduce CaLiSym, a lightweight framework that extends exact symplectic learning to such systems...