用Transformer模型精准模拟城市复杂环境下的三维风场，替代昂贵的流体计算。
Anchored-Branched Steady-state WInd Flow Transformer (AB-SWIFT): a metamodel for 3D atmospheric flow in urban environments
- 设计分叉结构Transformer，适配城市建筑变化与大网格数据
- 在多种大气稳定度下预测精度优于现有最先进模型
- 适合城市空气质量与风能规划研究者使用
局部尺度的空气流动建模对污染物扩散和风电场设计至关重要。为避免昂贵的计算流体动力学（CFD）模拟，深度学习代理模型成为有前景的替代方案。然而，在城市风场建模中，现有深度学习模型难以适应复杂的建筑几何变化和大规模网格。为此，我们提出锚定分叉稳态风流Transformer（AB-SWIFT），一种专为大气流动建模设计的分叉结构Transformer。我们在随机城市几何和不稳定、中性、稳定大气层结混合的仿真数据库上训练该模型。相比现有最先进Transformer与图神经网络模型，本模型在所有预测场上的精度均达到最优。代码与数据已公开于https://github.com/cerea-daml/abswift。
原文摘要 · Abstract (English)
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.
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