Generative Urban Flow Modeling: From Geometry to Airflow with Graph Diffusion

TL;DR

This paper introduces a novel generative diffusion model using graph neural networks to synthesize accurate urban wind flow fields from geometry data, enabling rapid and diverse airflow predictions for city planning.

Contribution

It presents the first framework combining graph neural networks with diffusion modeling to generate urban wind fields directly from geometry, improving efficiency and generalization over existing methods.

Findings

Model accurately recovers flow structures like wakes and recirculation zones.

Generalizes well to unseen geometries and wind conditions.

Demonstrates robustness to mesh variations and inference regimes.

Abstract

Urban wind flow modeling and simulation play an important role in air quality assessment and sustainable city planning. A key challenge for modeling and simulation is handling the complex geometries of the urban landscape. Low order models are limited in capturing the effects of geometry, while high-fidelity Computational Fluid Dynamics (CFD) simulations are prohibitively expensive, especially across multiple geometries or wind conditions. Here, we propose a generative diffusion framework for synthesizing steady-state urban wind fields over unstructured meshes that requires only geometry information. The framework combines a hierarchical graph neural network with score-based diffusion modeling to generate accurate and diverse velocity fields without requiring temporal rollouts or dense measurements. Trained across multiple mesh slices and wind angles, the model generalizes to unseen geometries, recovers key flow structures such as wakes and recirculation zones, and offers uncertainty-aware predictions. Ablation studies confirm robustness to mesh variation and performance under different inference regimes. This work develops is the first step towards foundation models for the built environment that can help urban planners rapidly evaluate design decisions under densification and climate uncertainty.