Papers
1–3 of 3Research Paper·Mar 6, 2026
Ensemble Graph Neural Networks for Probabilistic Sea Surface Temperature Forecasting via Input Perturbations
Accurate regional ocean forecasting requires models that are both computationally efficient and capable of representing predictive uncertainty. This work investigates ensemble learning strategies for ...
7.0 viability
Research Paper·Mar 10, 2026
CarbonBench: A Global Benchmark for Upscaling of Carbon Fluxes Using Zero-Shot Learning
Accurately quantifying terrestrial carbon exchange is essential for climate policy and carbon accounting, yet models must generalize to ecosystems underrepresented in sparse eddy covariance observatio...
4.0 viability
Research Paper·Mar 11, 2026
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 ...
4.0 viability