Brief Description: Supervised operator learning is an emerging machine learning paradigm with applications to modeling the evolution maps of spatio-temporal dynamical systems and approximating general black-box relationships between functional data. We propose a novel operator learning method, LOCA (Learning Operators with Coupled Attention), motivated from the attention mechanism. The input functions are mapped to a finite set of features which are then averaged with attention weights that depend on the output query locations. By coupling these attention weights together with an integral transform, LOCA is able to explicitly learn correlations in the target output functions, enabling us to approximate nonlinear operators even when the number of output function measurements is very small. Image: Caption: Reference Media Title: Reference Media: Intellectual Property Title: Intellectual Property: Intellectual Property: US2023-0214661A1 Docket Number: Docket#: 22-9935