Abstract :

Many learning problems require more than predicting an output from an individual input: the goal is to learn an operator that acts on functions and captures the relationship between input and output distributions. We propose Functional Newton Methods for Operator Learning, a framework that learns such operators through second-order optimization in function space. The resulting representation supports applications including regression, uncertainty quantification, and dependence assessment.