Conditional Flow Matching 秒级生成城市三维风温场，替代大涡模拟
Conditional Flow Matching for Generation of 3D Multi-variable Instantaneous Urban Microclimate Fields
用 CFM 几秒生成城市三维风温场，风速误差 2.99%，大涡模拟要跑很久的活它秒出，做城市微气候设计的可以看看。
论文提出用 Conditional Flow Matching（CFM）以建筑几何和平均流场为条件，生成三维瞬时风速与温度场，生成时间以秒计，而大涡模拟（LES）计算成本高、难以用于迭代设计。为绕开 3D 像素空间的显存瓶颈，模型在重叠像素空间并行运行，并用共享噪声初始化保持全域流场结构的空间连续性。对比 LES 参考数据，风速一阶统计量 NRMSE 为 2.99%，温度为 1.77%；二阶湍流指标 NRMSE 分别为 7.17% 和 8.84%，湍动能 NRMSE 为 7%。局部阵风预测案例验证了该代理模型在风工程中的速度与精度。
Conditional Flow Matching for Generation of 3D Multi-variable Instantaneous Urban Microclimate Fields
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.
