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${}^{\circ }$ global resolution able to accurately quantify both aleatoric and epistemic un…