AI RESEARCH
Frequency Bias and OOD Generalization in Neural Operators under a Variable-Coefficient Wave Equation
arXiv CS.LG
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ArXi:2605.12997v1 Announce Type: new Neural operators learn to map initial conditions to the terminal solution of partial differential equations (PDEs), providing a surrogate for the full operator mapping. This enables rapid prediction across different input configurations. While recent neural operator architectures have nstrated strong performance on diverse PDE tasks, their behavior under structured distribution shifts remains insufficiently understood.