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The Dynamic-Probabilistic Consistency Gap in Chaotic Surrogate Modeling
Finite-horizon probabilistic training creates a consistency gap that decouples uncertainty from local dynamics in chaotic surrogates; a Kalman-aware framework evaluating local innovations while transporting covariance through learned Jacobians closes it.
Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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