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[2601.11440] GenDA: Generative Data Assimilation on Complex Urban Areas via Classifier-Free Diffusion Guidance
Summary
The paper presents GenDA, a generative data assimilation framework for reconstructing urban wind fields from sparse sensor data, enhancing environmental monitoring capabilities.
Why It Matters
Urban wind flow reconstruction is crucial for assessing air quality and pedestrian comfort. GenDA offers a novel approach that improves accuracy in data assimilation, making it significant for urban planning and environmental science.
Key Takeaways
- GenDA utilizes a multiscale graph-based diffusion architecture for high-resolution wind field reconstruction.
- The framework integrates both unconditional and sensor-conditioned branches for improved accuracy.
- GenDA shows a 25-57% reduction in RRMSE compared to existing methods.
- The model is adaptable to various geometries and wind conditions without retraining.
- It provides a scalable solution for environmental monitoring in complex urban settings.
Computer Science > Machine Learning arXiv:2601.11440 (cs) [Submitted on 16 Jan 2026 (v1), last revised 17 Feb 2026 (this version, v2)] 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 View a PDF of the paper titled GenDA: Generative Data Assimilation on Complex Urban Areas via Classifier-Free Diffusion Guidance, by Francisco Giral and 4 other authors View PDF HTML (experimental) 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 pr...
