OneScience-Group/ShapeNetCar
ShapeNetCar Dataset Description The ShapeNetCar dataset comes from the paper Learning Three-dimensional Flow for Interactive Aerodynamic Design by Umetani and Bickel, published in ACM Transactions on Graphics (SIGGRAPH 2018). Based on three-dimensional car geometries from ShapeNet, the dataset uses CFD simulations to obtain velocity fields around the vehicles, surface pressure, and drag coefficients. It supports research on rapidly predicting aerodynamic physical… See the full description on the dataset page: https://huggingface.co/datasets/OneScience-Group/ShapeNetCar.
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1dataset: ShapeNetCar2root: data/mlcfd_data3format:4 - npy5 - vtk6 - txt7 - xlsx8 - png9statistics:10 stats/mean_in.npy:11 shape: [7]12 dtype: float3213 description_zh: 输入特征归一化均值14 stats/std_in.npy:15 shape: [7]16 dtype: float3217 description_zh: 输入特征归一化标准差18 stats/mean_out.npy:19 shape: [4]20 dtype: float3221 description_zh: 输出物理量归一化均值22 stats/std_out.npy:23 shape: [4]24 dtype: float3225 description_zh: 输出物理量归一化标准差26training_data:27 directory_pattern: training_data/param*28 required_files:29 Cd.npy: 阻力系数数组30 I1.npy: 输入参数数组31 I2.npy: 输入参数数组32 Press.npy: 表面压力数组33 Velo.npy: 速度场数组34 topo_hexquad.txt: 网格拓扑文本35preprocessed_data:36 directory_pattern: preprocessed_data/param*/<sample_id>37 required_files:38 x.npy:39 shape: [num_nodes, 7]40 dtype: float6441 description_zh: 节点输入特征，包含位置、SDF 和法向量等特征42 y.npy:43 shape: [num_nodes, 4]44 dtype: float6445 description_zh: 目标物理场，前三维为速度，最后一维为压力46 pos.npy:47 shape: [num_nodes, 3]48 dtype: float3249 description_zh: 节点三维坐标50 surf.npy:51 shape: [num_nodes]52 dtype: float6453 description_zh: 表面节点掩码54 edge_index.npy:55 shape: [2, num_edges]56 dtype: int6457 description_zh: 图边索引58notes_zh:59 - training_data/param0 到 param8 对应不同参数折。60 - preprocessed_data 中的样本目录由车辆几何样本 ID 命名。61 - Transolver-Car-Design 标准模型包默认读取 data/mlcfd_data/training_data、data/mlcfd_data/preprocessed_data 和 data/mlcfd_data/stats。62