English(EN) AeroChem: Closed-loop Physics-Informed State Space Modeling for Long-term Chemically-Reactive Air Quality Forecasting--Extended Version

AeroChem框架通过物理信息状态空间建模增强空气质量预测能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员推出了一种新颖的闭环框架AeroChem，用于长期空气质量预测，该框架将物理信息建模与状态空间方法相结合。该系统采用双分支协同校正设计，结合了基于UDE的物理先验过程分支和基于Mamba的时间上下文虚拟测量分支。通过使用神经卡尔曼融合，AeroChem在每个预测步骤中校正潜在物理状态，并将其反馈给模拟器以减轻长时预测漂移。该框架在预测突变方面表现出更高的稳定性和准确性，尤其是在新的HanoiAir数据集和公开基准测试中。 AI

影响该框架可以提高长期环境预测的准确性和稳定性，有助于公共卫生和政策决策。

排序理由该集群描述了一篇关于用于空气质量预测的新型建模框架的最新研究论文。[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) · Kien Le Trung, Tung Kieu, Dinh Duc Nha Nguyen, Cuong Do, Bin Yang, Phi Long Nguyen · 2026-10-15 04:00

AeroChem：闭环物理信息状态空间模型用于长期化学反应空气质量预测——扩展版

arXiv:2610.16630v1 Announce Type: cross Abstract: Accurate long-term air quality forecasting is challenging due to long-range transport, meteorological variability, and nonlinear chemical reactions underlying secondary pollutant formation. Purely data-driven models often suffer f…

本文如何被排名

报道来源 [1]

AeroChem：闭环物理信息状态空间模型用于长期化学反应空气质量预测——扩展版

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