English(EN) Global Attention with Linear Complexity for Exascale Generative Data Assimilation in Earth System Prediction

AI框架STORM以线性复杂度增强地球系统预测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

研究人员开发了STORM，一个新颖的生成式AI框架，通过将数据同化重新构建为基于扩散的贝叶斯后验采样，显著增强了地球系统预测能力。该方法用可扩展的AI推理取代了计算密集型的基于PDE的集合预报，并通过时空Transformer和全局注意力算法将复杂度从二次降低到线性。STORM展示了卓越的可扩展性，在Frontier超级计算机上实现了高强可扩展效率，并实现了用于不确定性量化的大成员集合，最终提高了飓风追踪和气候再分析的准确性。 AI

影响该框架通过实现更准确、更高效的数据同化，有可能显著推进气候建模和天气预报。

排序理由研究论文，详细介绍了用于地球系统预测的新AI框架。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Xiao Wang, Zezhong Zhang, Isaac Lyngaas, Hong-Jun Yoon, Jong-Youl Choi, Siming Liang, Janet Wang, Hristo G. Chipilski, Ashwin M. Aji, Feng Bao, Peter Jan van Leeuwen, Dan Lu, Guannan Zhang · 2026-09-02 04:00

面向地球系统预测的超大规模生成数据同化中的线性复杂度全局注意力机制

arXiv:2604.16590v2 Announce Type: replace-cross Abstract: Accurate Earth system prediction requires state inference from incomplete observations, but conventional two-stage data assimilation (DA) is computationally prohibitive because repeated PDE-based ensemble forecasts, observ…

本文如何被排名

报道来源 [1]

面向地球系统预测的超大规模生成数据同化中的线性复杂度全局注意力机制

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