Air flow modeling at a local scale is essential for applications such as pollutant dispersion modeling or wind farm modeling. To circumvent costly Computational Fluid Dynamics (CFD) computations, deep learning surrogate models have recently emerged as promising alternatives. However, in the context of urban air flow, deep learning models struggle to adapt to the high variations of the urban geometry and to large mesh sizes. To tackle these challenges, we introduce Anchored Branched Steady-state WInd Flow Transformer (AB-SWIFT), a transformer-based model with an internal branched structure uniquely designed for atmospheric flow modeling. We train our model on a specially designed database of atmospheric simulations around randomised urban geometries and with a mixture of unstable, neutral, and stable atmospheric stratifications. Our model reaches the best accuracy on all predicted fields compared to state-of-the-art transformers and graph-based models. Our code and data is available at https://github.com/cerea-daml/abswift.

翻译：局部尺度的空气流建模对于污染物扩散建模或风电场建模等应用至关重要。为规避计算成本高昂的计算流体动力学（CFD）计算，深度学习代理模型近期已成为颇具前景的替代方案。然而，在城市空气流场景中，深度学习模型难以适应城市几何结构的高度变化及大规模网格尺寸。针对这些挑战，我们提出了锚定分支稳态风场Transformer（AB-SWIFT）——一种基于Transformer架构、内部包含独特分支结构的模型，专为大气流建模而设计。我们基于一个专门构建的、涵盖随机城市几何结构及非稳定、中性、稳定大气层结混合状态的大气模拟数据集来训练该模型。相比现有最先进的Transformer模型与基于图的模型，我们的模型在所有预测场中均达到最优精度。代码与数据开源地址：https://github.com/cerea-daml/abswift。