GenDA: Generative Data Assimilation on Complex Urban Areas via Classifier-Free Diffusion Guidance
arXiv:2601.11440v3 Announce Type: replace 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 increasing sophistication of generative AI models, particularly diffusion architectures, is enabling specialized applications like high-resolution environmental reconstruction from sparse data.
This development allows for better understanding and management of complex urban environments, which is crucial for addressing climate-related challenges and infrastructure planning.
The ability to accurately reconstruct environmental data like wind fields with limited sensors improves urban planning, air quality monitoring, and disaster preparedness, reducing reliance on extensive physical sensor networks.
- · Smart city developers
- · Environmental monitoring agencies
- · Urban planners
- · AI/ML research in scientific computing
- · Traditional dense sensor network providers
- · Legacy environmental modeling software
Improved accuracy and efficiency in urban wind flow reconstruction using generative AI.
Enhanced capabilities for predictive modeling in urban environments, informing infrastructure and policy decisions.
Potential for broader application of generative data assimilation to other complex environmental or industrial monitoring challenges, reducing resource intensity for data collection.
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Read at arXiv cs.LG