arXiv:2602.04139v2 Announce Type: replace Abstract: Neural operators provide a powerful framework for learning discretization invariant mappings between function spaces, but standard deterministic models do not capture predictive uncertainty. We introduce diffusion last layer (DLL), a modular probabilistic output head for neural operator backbones. DLL represents target fields through an input dependent low rank expansion inspired by the Karhunen-Lo\'eve expansion and learns a conditional diffusion model over the corresponding coefficient space. This design enables efficient distributional mod
Source: arXiv cs.LG — read the full report at the original publisher.