用生成模型从稀疏传感器数据重建复杂城市风场，精度提升超五成。
GenDA: Generative Data Assimilation on Complex Urban Areas via Classifier-Free Diffusion Guidance
- 基于无分类器扩散机制，融合几何与观测信息进行风场重建。
- 在布里斯托尔真实街区测试中，误差降低25%-57%，结构相似度提升23%-33%。
- 无需重训练即可适配新地形、风向和传感器布局，适合城市环境监测。
城市风场重建对空气质量评估、热扩散分析和行人舒适度研究至关重要，但当仅有稀疏传感器数据时仍具挑战性。本文提出GenDA，一种生成式数据同化框架，可从有限观测中重建非结构化网格上的高分辨率风场。模型采用多尺度图结构扩散架构，基于计算流体动力学（CFD）模拟训练，并将无分类器引导解释为学习后的后验重构机制：无条件分支学习几何感知的流场先验，而传感器条件分支在采样过程中注入观测约束。该方法实现障碍物感知重建，并在不重新训练的前提下泛化至未见的网格几何、风向及传感器配置。同时适用于稀疏固定传感器与轨迹式观测。在英国布里斯托尔一个真实城区的雷诺平均纳维-斯托克斯（RANS）模拟上验证，特征雷诺数约为$\mathrm{Re}\approx2\times10^{7}$，涵盖复杂建筑结构与不规则地形。相比监督图神经网络（GNN）基线和经典降阶数据同化方法，GenDA将相对均方根误差（RRMSE）降低25%-57%，结构相似度指数（SSIM）提升23%-33%。该框架为复杂环境中可扩展的生成式、几何感知数据同化提供了可行路径。
原文摘要 · Abstract (English)
Urban wind flow reconstruction is essential for assessing air quality, heat dispersion, and pedestrian comfort, yet remains challenging when only sparse sensor data are available. We propose GenDA, a generative data assimilation framework that reconstructs high-resolution wind fields on unstructured meshes from limited observations. The model employs a multiscale graph-based diffusion architecture trained on computational fluid dynamics (CFD) simulations and interprets classifier-free guidance as a learned posterior reconstruction mechanism: the unconditional branch learns a geometry-aware flow prior, while the sensor-conditioned branch injects observational constraints during sampling. This formulation enables obstacle-aware reconstruction and generalization to held-out mesh geometries, wind directions, and sensor configurations within the studied urban-flow setting, without retraining. We consider both sparse fixed sensors and trajectory-based observations using the same reconstruction procedure. When evaluated against supervised graph neural network (GNN) baselines and classical reduced-order data assimilation methods, GenDA reduces the relative root-mean-square error (RRMSE) by 25-57% and increases the structural similarity index (SSIM) by 23-33% across the tested meshes. Experiments are conducted on Reynolds-averaged Navier-Stokes (RANS) simulations of a real urban neighborhood in Bristol, United Kingdom, at a characteristic Reynolds number of $\mathrm{Re}\approx2\times10^{7}$, featuring complex building geometry and irregular terrain. The proposed framework provides a scalable path toward generative, geometry-aware data assimilation for environmental monitoring in complex domains.
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