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arXiv AI By Abhijeet Vishwasrao, Sai Bharath Chandra Gutha, Andres Cremades, Klas Wijk, Aakash Patil, H. D. Lim, Christina Vanderwel, Catherine Gorle, Beverley J McKeon, Hossein Azizpour, Ricardo Vinuesa

Diff-SPORT: Diffusion-based Sensor Placement Optimization and Reconstruction of Turbulent flows in urban environments

Read the original on arXiv AI →

Diff-SPORT is a diffusion-based framework that integrates a generative diffusion prior, maximum a posteriori inference, and Shapley-value attribution to achieve high-fidelity reconstruction of turbulent flows and optimal sensor placement in urban environments. By training the diffusion prior once for a domain, it enables non-linear sensor placement and near-real-time flow reconstruction from sparse measurements, outperforming state-of-the-art methods and running orders of magnitude faster than RANS or LES simulations. The approach also generalizes to experimental passive scalar concentration data, demonstrating up to 57% lower reconstruction error than random sensor placement under extreme sparsity and providing compact, physically interpretable sensor configurations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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