AI RESEARCH

Recovering Physical Dynamics from Discrete Observations via Intrinsic Differential Consistency

arXiv CS.AI

ArXi:2605.08454v1 Announce Type: cross Recovering continuous-time dynamics from discrete observations is difficult because local supervision (e.g., pointwise regression targets, derivative approximations, or equation residuals) loses fidelity as the observation interval grows. We replace local supervision with a global structural constraint: any flow representing autonomous dynamics must satisfy the semi-group property under time translation. We train a time-conditioned secant velocity field whose deviation from this property, which we call Symmetry Rupture, serves two purposes. As a.