arXiv：cs.AI· Kien Le Trung, Tung Kieu, Dinh Duc Nha Nguyen, Cuong Do, Bin Yang, Phi Long Nguyen·· 12 小时前
AeroChem：面向长期化学反应性空气质量预报的闭环物理信息状态空间建模
AeroChem: Closed-loop Physics-Informed State Space Modeling for Long-term Chemically-Reactive Air Quality Forecasting--Extended Version
AI 导读
AeroChem 是面向化学反应性空气质量预报的闭环物理信息状态空间框架，采用 UDE 过程分支与 Mamba 虚拟测量分支的双分支协同校正设计，并通过 Neural Kalman Fusion 在每一步预报中修正潜在物理状态并反馈给模拟器，以缓解长时程漂移。作者同时发布融合污染物浓度、气象与排放清单的真实数据集 HanoiAir，实验显示其在长时程稳定性和突变预报上均有提升。
正文
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 from long-horizon drift and lack physical consistency, while existing physics-informed approaches commonly follow open-loop designs that fuse neural and physical components only at the output level. We propose AeroChem, a closed-loop physics-informed state space framework for chemically reactive air quality forecasting. AeroChem adopts a two-branch co-correction design: a UDE-based process branch provides a structured but imperfect physical prior, while a Mamba-based virtual measurement branch captures long-range temporal context and produces probabilistic virtual observations. Neural Kalman Fusion couples the two branches by correcting the latent physical state at each forecasting step and feeding the posterior state back into the simulator, reducing long-horizon drift. We further introduce HanoiAir, a real-world dataset integrating pollutant concentrations, meteorology, and emission inventories. Experiments on HanoiAir and a public benchmark show that AeroChem improves long-horizon stability and sudden-change forecasting.
| Comments: | 25 pages. An extended version of "AeroChem: Closed-loop Physics-Informed State Space Modeling for Long-term Chemically-Reactive Air Quality Forecasting--Extended Version" accepted at NeurIPS 2026 |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE) |
| Cite as: | arXiv:2610.16630 [cs.LG] |
| (or arXiv:2610.16630v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.16630 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Kien Le [view email]
[v1]
Mon, 12 Oct 2026 11:52:35 UTC (5,776 KB)
来源：arXiv：cs.AI · arxiv.org