用对称图网络+大规模仿真数据，高效精准预测建筑风荷载。
WindMiL: Equivariant Graph Learning for Wind Loading Prediction
- 基于符号距离函数生成建筑几何，用LES模拟462种工况构建数据集
- 提出反射等变图网络，使镜像结构预测结果保持物理一致性
- 预测均值与标准差误差小于0.02，镜像测试命中率超96%
准确预测建筑风荷载对结构安全和可持续设计至关重要，但传统风洞试验和大涡模拟（LES）成本高昂，单次计算需至少24小时，难以支持大规模参数研究。本文提出WindMiL框架，结合系统化数据生成与对称感知图神经网络（GNN）。首先，通过符号距离函数插值生成低层建筑屋顶几何，并在不同形状和风向条件下进行462组LES模拟，构建大规模风荷载数据集。其次，开发反射等变GNN，确保镜像几何下的预测结果具有一致性。在插值与外推评估中，WindMiL对表面压力系数均值和标准差的预测均达到高精度（如均值$C_p$的RMSE ≤ 0.02），在镜像测试中仍保持超过96%的命中率，而非等变基线模型下降超10%。通过系统数据与等变代理模型结合，WindMiL实现建筑风荷载的高效、可扩展且精准预测。
原文摘要 · Abstract (English)
Accurate prediction of wind loading on buildings is crucial for structural safety and sustainable design, yet conventional approaches such as wind tunnel testing and large-eddy simulation (LES) are prohibitively expensive for large-scale exploration. Each LES case typically requires at least 24 hours of computation, making comprehensive parametric studies infeasible. We introduce WindMiL, a new machine learning framework that combines systematic dataset generation with symmetry-aware graph neural networks (GNNs). First, we introduce a large-scale dataset of wind loads on low-rise buildings by applying signed distance function interpolation to roof geometries and simulating 462 cases with LES across varying shapes and wind directions. Second, we develop a reflection-equivariant GNN that guarantees physically consistent predictions under mirrored geometries. Across interpolation and extrapolation evaluations, WindMiL achieves high accuracy for both the mean and the standard deviation of surface pressure coefficients (e.g., RMSE $\leq 0.02$ for mean $C_p$) and remains accurate under reflected-test evaluation, maintaining hit rates above $96\%$ where the non-equivariant baseline model drops by more than $10\%$. By pairing a systematic dataset with an equivariant surrogate, WindMiL enables efficient, scalable, and accurate predictions of wind loads on buildings.
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