Learning Operators of Geometry with an Interface Autoencoder

Geometry-dependent PDEs define operators whose inputs and outputs may each consist of an oriented interface and a function on that interface, whereas most existing neural operators are formulated on fixed domains. In this paper, we first establish an approximation theorem for continuous operators be