AI RESEARCH

Semigroup Consistency as a Diagnostic for Learned Physics Simulators

arXiv CS.AI

ArXi:2605.26324v1 Announce Type: cross Learned physics simulators are often evaluated by one-step or short-horizon prediction error, but these metrics can miss failures in temporal composition and long-horizon rollout. For autonomous, state-complete systems, exact solution maps satisfy a semigroup law: direct evolution over $s+t$ should agree with evolution over $s$ followed by $t$. We propose normalized semigroup error as a post hoc, model-agnostic diagnostic comparing these direct and composed learned predictions.