GenDA: Generative Data Assimilation on Complex Urban Areas via Classifier-Free Diffusion Guidance

• URL: http://arxiv.org/abs/2601.11440v1
• Date: Fri, 16 Jan 2026 17:02:00 GMT
• Title: GenDA: Generative Data Assimilation on Complex Urban Areas via Classifier-Free Diffusion Guidance
• Authors: Francisco Giral, Álvaro Manzano, Ignacio Gómez, Ricardo Vinuesa, Soledad Le Clainche,
• Abstract summary: GenDA is a generative data assimilation framework that reconstructs high-resolution wind fields on unstructured meshes from limited observations.<n>The proposed framework provides a scalable path toward generative, geometry-aware data assimilation for environmental monitoring in complex domains.
• Score: 5.646118100261389
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 across unseen geometries, wind directions, and mesh resolutions 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 neighbourhood 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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