arXiv Machine Learning July 1, 2026 By Janne Perini, Rafael Bischof, Moab Arar, Ay\c{c}a Duran, Michael A. Kraus, Siddhartha Mishra, Bernd Bickel

Pretrained Video Models as Differentiable Physics Simulators for Urban Wind Flows

Read the original on arXiv Machine Learning →

arXiv:2603. 21210v3 Announce Type: replace Abstract: Designing urban spaces that provide pedestrian wind comfort and safety requires time-resolved Computational Fluid Dynamics (CFD) simulations, but their current computational cost makes extensive design exploration impractical.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jul 10

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

arXiv:2601. 11440v3 Announce Type: replace-cross 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.

By Francisco Giral, \'Alvaro Manzano, Ignacio G\'omez, Ricardo Vinuesa, Soledad Le Clainche

diffusion

arXiv Machine Learning

Jun 8

Drifting Models for Surrogate Flow Modeling

arXiv:2606. 07481v1 Announce Type: new Abstract: While Computational Fluid Dynamics (CFD) provides high-fidelity flow fields for optimizing indoor environments, its computational cost limits rapid exploration.

By Chris R. Jung, Markus D\"orr, Natalie J\"ungling, Jennifer Niessner, Adam T. M\"uller, Nicolaj C. Stache

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arXiv AI

Jul 10

LLT: Local Linear Transformer for PDE Operator Learning

arXiv:2607. 07718v1 Announce Type: cross Abstract: Neural operators have become a common approach for learning PDE solution maps and accelerating numerical simulations.

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arXiv Machine Learning

Jun 4

U-Net-Accelerated Quality-Diversity Optimization for Climate-Adaptive Urban Layouts

arXiv:2606. 04658v1 Announce Type: cross Abstract: Optimizing urban layouts for climate adaptation requires balancing building density with cold-air ventilation.

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arXiv AI

Jun 10

Geometry-Aware Anisotropic Boundary Correction for Aerodynamic Simulation

arXiv:2606. 09963v1 Announce Type: cross Abstract: Aerodynamic simulation is a key component of engineering shape design, where core quantities such as the surface pressure coefficient strongly depend on flow dynamics near solid boundaries.

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arXiv Machine Learning

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A Validated LBM Dataset and Pipeline for Surrogate Modeling of Turbulent 3D Obstructed Channel Flows

arXiv:2606. 16765v1 Announce Type: new Abstract: Evaluating neural operators for 3D turbulent flow requires validated datasets with physical benchmarks.

By Lukas Schr\"oder, Shubham Kavane, Harald K\"ostler

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