WindMiL: Equivariant Graph Learning for Wind Loading Prediction

Accurate prediction of wind loading on buildings is critical 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. We introduce \textsc{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 low-rise building aerodynamics 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, \textsc{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 non-equivariant baselines drop by more than $10\%$. By pairing a systematic dataset with an equivariant surrogate, \textsc{WindMiL} enables efficient, scalable, and physically consistent prediction of wind loads on buildings.