Data-Driven Integration Kernels for Interpretable Nonlocal Operator Learning

ArXi:2603.10305v1 Announce Type: new Machine learning models can represent climate processes that are nonlocal in horizontal space, height, and time, often by combining information across these dimensions in highly nonlinear ways. While this can improve predictive skill, it makes learned relationships difficult to interpret and prone to overfitting as the extent of nonlocal information grows. We address this challenge by