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cs.LG2026
Data-Driven Integration Kernels for Interpretable Nonlocal Operator Learning
Savannah L. Ferretti, Jerry Lin, Sara Shamekh +3
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 no…
cs.LG2023
ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation
Sungduk Yu, Zeyuan Hu, Akshay Subramaniam +44
Modern climate projections lack adequate spatial and temporal resolution due to computational constraints, leading to inaccuracies in representing critical processes like thunderst…