标题： 高效数据的复杂建筑几何体城市气流和温度场快速预测 显示英文标题

标题： Data-efficient rapid prediction of urban airflow and temperature fields for complex building geometries

摘要： 基于建筑几何准确预测城市微气候，包括风速和温度，需要捕捉建筑与气流之间的复杂相互作用，尤其是受方向几何影响的远距离尾流效应。 传统依赖计算流体动力学（CFD）的方法在大规模模拟中成本过高，而数据驱动的方法则在有限的训练数据和建模局部及远场依赖关系方面存在困难。 为此，我们提出了一种新框架，结合多方向距离特征（MDDF）和局部训练，以最少的CFD数据实现有效的风场预测。 通过降低问题的维度，局部训练有效增加了训练样本的数量，而MDDF编码了周围的几何信息，以准确建模尾流动力学和气流转向。 仅在24个CFD模拟上进行训练，我们的局部傅里叶神经算子（Local-FNO）模型在不到一分钟内生成完整的三维风速和温度预测，相比传统CFD方法提高了500倍的速度。 在未见过的城市配置中，我们的方法在风速上的平均绝对误差为0.3 m/s，温度为0.3$^{\circ}$C，展示了强大的泛化能力，并在实际城市应用中具有显著潜力。 显示英文摘要

摘要： Accurately predicting urban microclimate, including wind speed and temperature, based solely on building geometry requires capturing complex interactions between buildings and airflow, particularly long-range wake effects influenced by directional geometry. Traditional methods relying on computational fluid dynamics (CFD) are prohibitively expensive for large-scale simulations, while data-driven approaches struggle with limited training data and the need to model both local and far-field dependencies. In response, we propose a novel framework that leverages a multi-directional distance feature (MDDF) combined with localized training to achieve effective wind field predictions with minimal CFD data. By reducing the problem's dimensionality, localized training effectively increases the number of training samples, while MDDF encodes the surrounding geometric information to accurately model wake dynamics and flow redirection. Trained on only 24 CFD simulations, our localized Fourier neural operator (Local-FNO) model generates full 3D wind velocity and temperature predictions in under one minute, yielding a 500-fold speedup over conventional CFD methods. With mean absolute errors of 0.3 m/s for wind speed and 0.3 $^{\circ}$C for temperature on unseen urban configurations, our method demonstrates strong generalization capabilities and significant potential for practical urban applications.