Predicting Out-of-Distribution Generalization of Neural Operators via Observable Spectral Error Decomposition

Neural operators have emerged as powerful surrogates for solving partial differential equations (PDEs), yet their reliability under distribution shift remains a critical barrier to deployment. Existing approaches to out-of-distribution (OOD) generalization in operator learning are largely empirical