Paper ID: 2410.01746

Leray-Schauder Mappings for Operator Learning

Emanuele Zappala

We present an algorithm for learning operators between Banach spaces, based on the use of Leray-Schauder mappings to learn a finite-dimensional approximation of compact subspaces. We show that the resulting method is a universal approximator of (possibly nonlinear) operators. We demonstrate the efficiency of the approach on two benchmark datasets showing it achieves results comparable to state of the art models.

Submitted: Oct 2, 2024