Data-Driven Integration Kernels for Interpretable Nonlocal Operator Learning
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 introducing data-driven integration kernels, a framework that adds structure to nonlocal operator learning by explicitly separating nonlocal information aggregation from local nonlinear prediction. Each spatiotemporal predictor field is first integrated using learnable kernels (defined as continuous weightin...
| Authors | Savannah L. Ferretti, Jerry Lin, Sara Shamekh et al. |
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| Primary category | cs.LG |
| Published | 11 March 2026 |
| Last updated at source | 17 September 2026 |
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