Handling unstructured data for operator learning using implicit neural representations

Operator learning methods are too often constrained by a fixed sampling of both the input and output functions. We propose a novel method to allow current operator learning methods to learn on any sampling. We show that our method can perform inference on unseen samplings, and that it allows returning outputs as continuous functions.

In ICLR 2023 Tiny Papers Track