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cs.LGcs.CEJanne Perini, Rafael Bischof, Moab Arar et al.
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Mar 22, 2026
WinDiNet repurposes the LTX-Video latent diffusion transformer as a fast, differentiable surrogate for urban wind flow simulation, addressing the prohibitive cost of time-resolved CFD in design exploration. By fine-tuning the 2B-parameter video model on 10,000 2D incompressible CFD simulations over procedurally generated building layouts, the authors achieve sub-second generation of 112-frame rollouts while enabling end-to-end gradient-based optimization of building positions for pedestrian wind comfort.
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. We introduce WinDiNet (Wind Diffusion Network), a pretrained video diffusion model that is repurposed as a fast, differentiable surrogate for this task. Starting from LTX-Video, a 2B-parameter latent video transformer, we fine-tune on 10,000 2D incompressible CFD simulations over procedurally generated building layouts. A systematic study of training regimes, conditioning mechanisms, and VAE adaptation strategies, including a physics-informed decoder loss, identifies a configuration that outperforms purpose-built neural PDE solvers. The resulting model generates full 112-frame rollouts in under a second. As the surrogate is end-to-end differentiable, it doubles as a physics simulator for gradient-based inverse optimization: given an urban footprint layout, we optimize building positions directly through backpropagation to improve wind safety as well as pedestrian wind comfort. Experiments on single- and multi-inlet layouts show that the optimizer discovers effective layouts even under challenging multi-objective configurations, with all improvements confirmed by ground-truth CFD simulations.
cs.CEcs.LGTaiga Saito, Yu Otake, Daijiro Mizutani et al.
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Mar 22, 2026
This exploratory study investigates using TabPFN—a transformer-based tabular foundation model—and its extension library for geotechnical site characterization. The core idea is to leverage in-context learning to perform soil classification and multivariate parameter imputation without model retraining or hyperparameter tuning, while obtaining interpretable insights through embeddings, posterior distributions, and SHAP analysis. This matters because geotechnical engineering requires uncertainty-aware, interpretable predictions for safety-critical decisions, yet faces severe data scarcity.
Geotechnical site characterisation relies on sparse, heterogeneous borehole data where uncertainty quantification and model interpretability are as critical as predictive accuracy for reliable engineering decisions. This paper presents an exploratory investigation into the use of TabPFN, a transformer-based tabular foundation model using in-context learning, and its extension library tabpfn-extensions for two geotechnical inference tasks: (1) soil-type classification using N-value and shear-wave velocity data from a synthetic geotechnical dataset, and (2) iterative imputation of five missing mechanical parameters ($s_\mathrm{u}$, $E_{\mathrm{u}}$, ${\sigma'}_\mathrm{p}$, $C_\mathrm{c}$, $C_\mathrm{v}$) in benchmark problem BM/AirportSoilProperties/2/2025. We apply cosine-similarity analysis to TabPFN-derived embeddings, visualise full posterior distributions from an iterative inference procedure, and compute SHAP-based feature importance, all without model retraining. Learned embeddings clearly separate Clay and Sand samples without explicit soil-type supervision; iterative imputation improves predictions for four of five target parameters, with posterior widths that reflect physically reasonable parameter-specific uncertainty; and SHAP analysis reveals the inter-parameter dependency structure, recovering established geotechnical relationships including the Skempton compression index correlation and the inverse dependence of preconsolidation pressure on water content. These results suggest the potential of foundation-model-based tools to support interpretable, uncertainty-aware parameter inference in data-scarce geotechnical practice.