English(EN) 4D Parallelism Unlocks Exascale Bayesian Neural Networks for High-Fidelity Atmospheric Modeling

AI模型BEAST通过新颖的4D并行实现高保真大气预报

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-14 04:00

研究人员开发了BEAST，一种用于大气预报的贝叶斯Swin Transformer模型，能够量化不确定性。该模型利用新颖的4D并行方案，在JUPITER超级计算机上高效训练了一个24亿参数的模型，并取得了高性能。BEAST在40年的数据上进行训练，展示了具有竞争力的预测能力，并能以极高的精度预测极端事件，在生成大型集合预报的速度上优于当前的人工智能和数值模型。 AI

影响通过实现高保真不确定性量化和更快的极端事件预测，推动了气候与地球系统科学领域的人工智能能力。

排序理由研究论文，详细介绍了一种用于大气建模的新型人工智能模型和并行技术。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Deifilia Kieckhefen, Juan Pedro Guti\'errez Hermosillo Muriedas, Lars Helge Heyen, Mathis Bode, Iida Hakulinen, Andreas Herten, Chelsea Maria John, Thorsten Kurth, Anni Moisala, Asena Karolin \"Ozdemir, Kaleb Phipps, Oskar Taubert, Arvid Weyrauch, Markus… · 2026-09-14 04:00

四维并行实现百亿亿次贝叶斯神经网络用于高保真大气建模

arXiv:2609.12815v1 Announce Type: cross Abstract: 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 uncertainty. To overcome the associated computationa…

本文如何被排名

报道来源 [1]

四维并行实现百亿亿次贝叶斯神经网络用于高保真大气建模

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