The Cost of a Physics Prior Is Bounded by the Ablation Gap
Read the original on arXiv AI →The paper establishes a theoretical bound on the cost of enforcing a physics prior in machine learning models, showing that the excess risk of a shape‑constrained hypothesis class is always bounded by the excess risk of an ablated model that ignores the prior. Empirical tests on an ordinal wildfire‑severity task confirm that a constrained model can never be outperformed by its own ablation, and that the cost of the prior is protocol‑dependent and can be quantified using a self‑calibrating floor. The authors also propose a two‑fit screening method to reject unidentifiable experiments before training a constrained model.
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