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arXiv Machine Learning By Eric Fock

Detecting and Discriminating Operator Misspecification in Hybrid PDE-Parameter Learning: a Reference-Free Instrument, with Discrimination Bounded In Sample

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The paper introduces a reference‑free instrument that, from a single fit and without an oracle, can detect whether a hybrid PDE‑parameter estimator’s assumed operator is misspecified and distinguish this from mere parameter unidentifiability. In a self‑adjoint parabolic inverse problem, the proposed information‑matrix statistic correctly identifies misspecification with low false‑positive rates, while remaining silent when the design is correctly specified but non‑identifiable. The study demonstrates that conventional accuracy checks can miss significant operator errors, and it maps out the instrument’s blind spots and conditions under which its guarantees hold.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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