Conditional Flow Matching for Generation of 3D Multi-variable Instantaneous Urban Microclimate Fields
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.
Related
- Scenario-conditioned flow matching for probabilistic generation of three-component ground-motion waveforms
- Conditional Mean Flows for Frequency-Domain Scattered Wavefield Generation
- Microclimate persistence in island montane cloud forests
- Trapped in a hot microcosm: how flower microclimate contributes to pollination syndrome in a thermogenic system
- Terrestrial lidar versus field surveys to assess microclimate temperature buffering in forests
BibTeX
@misc{liu2026conditional,
title = {{Conditional Flow Matching for Generation of 3D Multi-variable Instantaneous Urban Microclimate Fields}},
author = {Peng Liu and Shaoxiang Qin and Theodore Potsis and Lili Ji and Dingyang Geng and Liangzhu Leon Wang},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.10430},
eprint = {2610.10430},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.10430},
}
RIS
TY - UNPB
TI - Conditional Flow Matching for Generation of 3D Multi-variable Instantaneous Urban Microclimate Fields
AU - Peng Liu
AU - Shaoxiang Qin
AU - Theodore Potsis
AU - Lili Ji
AU - Dingyang Geng
AU - Liangzhu Leon Wang
PY - 2026
DA - 2026/10/07
PB - arXiv
DO - 10.48550/arxiv.2610.10430
UR - https://arxiv.org/abs/2610.10430
AB - 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.
ER -