Physics is the Best Teacher: Consistency Learning for Time-Invariant Operators of Chaotic Dynamics
Read the original on arXiv Machine Learning →The paper proposes a method for predicting long‑term behavior in chaotic systems by learning a time‑invariant evolution operator that can jump across large time spans in a single evaluation. It derives consistency equations that the operator must satisfy, incorporating differential and compositional objectives tied to physical dynamics. Experiments on five chaotic systems show that this physics‑distilled consistency approach yields more accurate short‑term trajectories and long‑term statistics, survives temporal extrapolation, and requires only one‑tenth the evaluations of autoregressive rollouts.
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