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physics.ao-ph2026
4D Parallelism Unlocks Exascale Bayesian Neural Networks for High-Fidelity Atmospheric Modeling
Deifilia Kieckhefen, Juan Pedro Gutiérrez Hermosillo Muriedas, Lars Helge Heyen +12
We present BEAST, the first-ever Bayesian Swin Transformer for atmospheric forecasting on 0.25 global resolution able to accurately quantify both aleatoric and epistemic un…
cs.AI2026
Differentiable Power-Flow Optimization
Muhammed Öz, Jasmin Hörter, Kaleb Phipps +3
With the rise of renewable energy sources and their high variability in generation, the management of power grids becomes increasingly complex and computationally demanding. Conven…