arXiv：cs.LG· Peng Liu, Shaoxiang Qin, Theodore Potsis, Lili Ji, Dingyang Geng, Liangzhu Leon Wang·· 4 小时前AI 评分36
基于条件流匹配的3D多变量城市微气候瞬时场生成
Conditional Flow Matching for Generation of 3D Multi-variable Instantaneous Urban Microclimate Fields
AI 导读
研究采用条件流匹配（CFM）生成框架，以建筑几何和平均流为指导，在数秒内生成城市微气候的三维瞬时风速与温度场。模型在重叠像素空间并行运行并通过共享噪声初始化保持全域流场结构连续性，对参考LES数据的一阶统计量NRMSE为风速2.99%、温度1.77%，二阶湍流指标NRMSE为风速7.17%、温度8.84%，湍动能NRMSE为7%。
正文
Abstract:Rapid and accurate prediction of urban wind and temperature fields is important for urban microclimate design and climate adaptation. Large-eddy simulation (LES) effectively resolves these instantaneous fields, but its application is limited in iterative design of urban microclimate applications due to high computational cost. Existing regressive data-driven models offers quick outputs, but they produce only deterministic point predictions that inherently fail to represent turbulent stochasticity. This paper adopts a novel generative framework of Conditional Flow Matching (CFM) that uses building geometry and mean flow as guidance to generate plausible three-dimensional instantaneous velocity and temperature fields for urban microclimate in seconds. To overcome the GPU memory bottleneck of pixel space 3D generation, the model operates in parallel on overlapping pixel space through a shared-noise initialization that preserves high spatial continuity of flow structure across the entire domain. Against reference LES data, the CFM surrogate can rapidly and accurately restore the first-order statistics with Normalized Root Mean Square Error (NRMSE) of 2.99% for wind and 1.77% for temperature, second-order turbulence metrics with NRMSE of 7.17% for wind and 8.84% for temperature, turbulent kinetic energy with NRMSE of 7%, probability density function and vertical profiles in representative locations. Wind engineering application of local gust prediction demonstrate that the speed and accuracy of CFM, supporting the use of generative AI for making turbulence-aware resilient urban design and climate adaptation more computationally feasible.
| Subjects: | Fluid Dynamics (physics.flu-dyn); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG); Atmospheric and Oceanic Physics (physics.ao-ph) |
| Cite as: | arXiv:2610.10430 [physics.flu-dyn] |
| (or arXiv:2610.10430v1 [physics.flu-dyn] for this version) | |
| https://doi.org/10.48550/arXiv.2610.10430 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Liangzhu Leon Wang [view email]
[v1]
Wed, 7 Oct 2026 17:09:48 UTC (3,936 KB)
来源：arXiv：cs.LG · arxiv.org