Kernel-based Operator Learning: Error Analysis, Budget Allocation, and a Physics-Informed Extension

We study kernel-based operator learning in a two-stage sampling framework, where an offline kernel regression operator learns a discretized representation of the target operator from input-output pairs and an online kernel reconstruction operator recovers the output function from predicted observati...