Advancing Operator Learning with Regularized Random Features and Finite Elements

This research explores a novel approach to operator learning, combining regularized random Fourier features and finite element methods within the framework of Sobolev spaces. The paper likely contributes to the theoretical understanding and practical implementation of learning operators, potentially impacting fields such as scientific computing and physics simulation.