2 papers
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.LG2024
AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability
Stefan Meisenbacher, Kaleb Phipps, Oskar Taubert +4
Optimizing smart grid operations relies on critical decision-making informed by uncertainty quantification, making probabilistic forecasting a vital tool. Designing such forecastin…