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README.md

@@ -0,0 +1,71 @@
+---
+license: cc-by-4.0
+#User-Defined Tags
+tags:
+  - ShapeNetCar
+  - 3D Aerodynamic Flow Field Prediction
+language:
+  - en
+  - zh
+---
+<p align="center">
+  <strong>
+    <span style="font-size: 30px;">ShapeNetCar</span>
+  </strong>
+</p>
+
+## Dataset Description
+
+The ShapeNetCar dataset comes from the paper [Learning Three-dimensional Flow for Interactive Aerodynamic Design](https://doi.org/10.1145/3197517.3201325) 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 fields and forces from three-dimensional geometry.
+
+
+## Supported Tasks
+
+This standardized data repository is organized for the ShapeNetCar external flow field prediction task. It contains raw training data, preprocessed graph data, normalization statistics, linear regression utility code, paper figure and table data, and result comparison plots. It can be used for training, inference, evaluation, and visualization with the OneScience/Transolver-Car-Design model. This repository contains 889 car geometry samples grouped from `param0` through `param8`. It retains the raw CFD arrays, meshes, and VTK files while also providing preprocessed samples for graph neural network models. Each preprocessed sample consists of node features, target physical fields, three-dimensional coordinates, a surface mask, and graph edge indices.
+
+
+
+## Dataset Format and Structure
+
+In the preprocessed graph data, each `preprocessed_data/param*/<sample_id>/` directory contains:
+
+| File | shape | dtype | Description |
+|---|---:|---|---|
+| `x.npy` | `[num_nodes, 7]` | `float64` | Node input features, including position, signed distance function (SDF), normals, and other features |
+| `y.npy` | `[num_nodes, 4]` | `float64` | Target physical fields; the first three channels are velocity components and the last channel is pressure |
+| `pos.npy` | `[num_nodes, 3]` | `float32` | Three-dimensional node coordinates |
+| `surf.npy` | `[num_nodes]` | `float64` | Surface node mask |
+| `edge_index.npy` | `[2, num_edges]` | `int64` | Edge indices of the graph structure |
+
+Normalization statistics files are located in `stats/`:
+
+| File | shape | dtype | Description |
+|---|---:|---|---|
+| `mean_in.npy` | `[7]` | `float32` | Mean of the input features |
+| `std_in.npy` | `[7]` | `float32` | Standard deviation of the input features |
+| `mean_out.npy` | `[4]` | `float32` | Mean of the output physical quantities |
+| `std_out.npy` | `[4]` | `float32` | Standard deviation of the output physical quantities |
+
+## How to Use the Dataset
+
+This dataset is compatible with the `OneScience-Sugon/Transolver-Car-Design` model. At runtime, scripts in the model package can organize the data from this repository under `data/`.
+
+- Files and Download:
+
+```bash
+hf download --dataset OneScience-Sugon/ShapeNetCar --local-dir ./data
+```
+
+
+## Official OneScience Information
+
+| Platform | OneScience Main Repository | Skills Repository |
+|---|---|---|
+| Gitee  | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills |
+| GitHub  | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills |
+
+## Citation and License
+
+- This dataset is organized or converted from the ShapeNetCar three-dimensional aerodynamic flow field data released by Nobuyuki Umetani and Bernd Bickel. When using it, please cite the original ShapeNetCar paper: [Learning Three-dimensional Flow for Interactive Aerodynamic Design](https://doi.org/10.1145/3197517.3201325)
+- If this dataset is used to reproduce the Transolver car aerodynamic design experiments, please also cite the original Transolver paper: [Transolver: A Fast Transformer Solver for PDEs on General Geometries](https://arxiv.org/abs/2402.02366)
+- This repository retains source attribution and is organized for automated OneScience ModelScope execution scenarios. Before public distribution, please verify the license requirements of the upstream project.

README_zh.md

@@ -0,0 +1,71 @@
+---
+license: CC-BY-4.0
+#用户自定义标签
+tags:
+  - ShapeNetCar
+  - 3D Aerodynamic Flow Field Prediction
+language:
+  - en
+  - zh
+---
+<p align="center">
+  <strong>
+    <span style="font-size: 30px;">ShapeNetCar</span>
+  </strong>
+</p>
+
+## 数据集描述
+
+ShapeNetCar 数据集来源于 Umetani 和 Bickel 发表于 ACM Transactions on Graphics（SIGGRAPH 2018）的论文 [《Learning Three-dimensional Flow for Interactive Aerodynamic Design》](https://doi.org/10.1145/3197517.3201325)。该数据集以 ShapeNet 中的汽车三维几何为基础，通过 CFD 仿真获得车辆周围的速度场、表面压力和阻力系数，用于研究由三维几何快速预测空气动力学物理场及气动力的方法。
+
+
+## 数据集支持的任务
+
+本标准化数据仓库面向 ShapeNetCar 汽车外流场预测任务整理，仓库包含原始训练数据、预处理图数据、归一化统计量、线性回归辅助代码、论文图表数据及结果对比图，可用于 OneScience/Transolver-Car-Design 模型的训练、推理、评估和可视化。本仓库收录 889 个汽车几何样本，按 `param0` 至 `param8` 分组，既保留原始 CFD 数组、网格和 VTK 文件，也提供面向图神经网络模型的预处理样本。每个预处理样本由节点特征、目标物理场、三维坐标、表面掩码和图边索引组成。
+
+
+
+## 数据集的格式和结构
+
+预处理图数据中，每个 `preprocessed_data/param*/<sample_id>/` 目录包含：
+
+| 文件 | shape | dtype | 说明 |
+|---|---:|---|---|
+| `x.npy` | `[num_nodes, 7]` | `float64` | 节点输入特征，包含位置、符号距离函数（SDF）和法向量等特征 |
+| `y.npy` | `[num_nodes, 4]` | `float64` | 目标物理场，前三个通道为速度分量，最后一个通道为压力 |
+| `pos.npy` | `[num_nodes, 3]` | `float32` | 节点三维坐标 |
+| `surf.npy` | `[num_nodes]` | `float64` | 表面节点掩码 |
+| `edge_index.npy` | `[2, num_edges]` | `int64` | 图结构的边索引 |
+
+归一化统计文件位于 `stats/`：
+
+| 文件 | shape | dtype | 说明 |
+|---|---:|---|---|
+| `mean_in.npy` | `[7]` | `float32` | 输入特征均值 |
+| `std_in.npy` | `[7]` | `float32` | 输入特征标准差 |
+| `mean_out.npy` | `[4]` | `float32` | 输出物理量均值 |
+| `std_out.npy` | `[4]` | `float32` | 输出物理量标准差 |
+
+## 数据集使用方式
+
+本数据集适配 `OneScience-Sugon/Transolver-Car-Design` 模型。模型运行时可通过模型包脚本将本仓库的数据组织到 `data/`下。
+
+- 文件与下载：
+
+```bash
+hf download --dataset OneScience-Sugon/ShapeNetCar --local-dir ./data
+```
+
+
+## OneScience 官方信息
+
+| 平台 | OneScience 主仓库 | Skills 仓库 |
+|---|---|---|
+| Gitee  | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills |
+| GitHub  | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills |
+
+## 引用与许可证
+
+- 本数据集基于 Nobuyuki Umetani 和 Bernd Bickel 发布的 ShapeNetCar 三维气动流场数据进行整理或转换，使用时请引用 ShapeNetCar 原始论文：[Learning Three-dimensional Flow for Interactive Aerodynamic Design](https://doi.org/10.1145/3197517.3201325)
+- 若本数据集用于复现 Transolver 的汽车气动设计实验，请同时引用 Transolver 原始论文：[Transolver: A Fast Transformer Solver for PDEs on General Geometries](https://arxiv.org/abs/2402.02366)
+- 本仓库保留来源说明，并面向 OneScience ModelScope 自动运行场景进行整理；公开分发前请根据上游项目确认许可证要求。

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data/mlcfd_data/linear_regression_code/learn_linear.py

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+import os,sys
+import numpy
+import random
+import math
+import torch
+
+def splitScalar_NFold(X,Y,N_fold):
+	nt = X.shape[0]
+	assert Y.shape[0] == nt
+
+	aX = []
+	aY = []
+	for j in range(N_fold):
+		aX.append(X[j])
+		aY.append(numpy.array([Y[j]]))
+	for i in range(N_fold,nt):
+		j = random.randint(0,N_fold-1)
+		aX[j] = numpy.vstack( (aX[j],X[i,:]) )
+		aY[j] = numpy.append(aY[j],Y[i])		
+	return aX,aY
+
+def split_train_validate(aX,aT,ind_fold):
+	N = len(aX)
+	m = aX[0].shape[1]
+	Xt = numpy.empty((0,m))
+	Tt = numpy.empty((0,))
+	Xv = numpy.empty((0,m))
+	Tv = numpy.empty((0,))
+	for j in range(N):
+		if j == ind_fold:
+			Xv = numpy.vstack((Xv,aX[j]))
+			Tv = numpy.append(Tv,aT[j])
+		else:
+			Xt = numpy.vstack((Xt,aX[j]))
+			Tt = numpy.append(Tt,aT[j])	
+	Xt = Xt.astype(numpy.float32)
+	Tt = Tt.astype(numpy.float32)	
+	Xv = Xv.astype(numpy.float32)
+	Tv = Tv.astype(numpy.float32)	
+	return Xt,Tt,Xv,Tv
+
+def LeastSquare_SVD(X,Y,alpha0):
+	nt = X.shape[0]
+	one = numpy.ones((nt,1))
+	X1 = numpy.hstack((X,one))
+	Ux, Dx, Vx = numpy.linalg.svd(X1, full_matrices=False)
+	nt0 = Dx.shape[0]
+	for i in range(nt0):
+		x = Dx[i]
+		Dx[i] = x/(x*x+alpha0)
+	DxInv = numpy.diag(Dx)
+	W0 = numpy.dot(numpy.dot(Vx.T, DxInv), Ux.T)
+	W1 = Y
+	print("W0: "+str(W0.shape))
+	print("w1: "+str(W1.shape))
+	return W0,W1
+
+def LeastSquare_SVD_Eval(X,W0,W1):
+	nt = X.shape[0]
+	one = numpy.ones((nt,1))
+	X1 = numpy.hstack((X,one))
+	XW0 = numpy.dot(X1,W0)
+	XW0W1 = numpy.dot(XW0,W1)
+	return XW0W1
+
+
+def loadXT():
+	X = numpy.load("I2_0.npy")	
+	X = numpy.vstack((X,numpy.load("I2_1.npy")))
+	X = numpy.vstack((X,numpy.load("I2_2.npy")))
+	X = numpy.vstack((X,numpy.load("I2_3.npy")))
+	X = numpy.vstack((X,numpy.load("I2_4.npy")))
+	X = numpy.vstack((X,numpy.load("I2_5.npy")))
+	X = numpy.vstack((X,numpy.load("I2_6.npy")))
+	X = numpy.vstack((X,numpy.load("I2_7.npy")))
+	X = numpy.vstack((X,numpy.load("I2_8.npy")))
+
+	T = numpy.load("Cd_0.npy")
+	T = numpy.append(T, numpy.load("Cd_1.npy"))	
+	T = numpy.append(T, numpy.load("Cd_2.npy"))	
+	T = numpy.append(T, numpy.load("Cd_3.npy"))	
+	T = numpy.append(T, numpy.load("Cd_4.npy"))	
+	T = numpy.append(T, numpy.load("Cd_5.npy"))	
+	T = numpy.append(T, numpy.load("Cd_6.npy"))	
+	T = numpy.append(T, numpy.load("Cd_7.npy"))	
+	T = numpy.append(T, numpy.load("Cd_8.npy"))	
+	return X,T
+
+def main():
+	X,T = loadXT()
+	assert( X.shape[0] == T.shape[0] )
+
+	N_fold = 9
+	N_sample = 4
+	ES = []
+	for ind_sample in range(N_sample):
+		aX,aT = splitScalar_NFold(X,T,N_fold)
+		for ind_fold in range(N_fold):
+			print("n-fold split",ind_sample,ind_fold,aX[ind_fold].shape," ",aT[ind_fold].shape)
+		E = []
+		for ind_fold in range(N_fold):
+			Xt,Tt,Xv,Tv = split_train_validate(aX,aT,ind_fold)
+			print(ind_sample,ind_fold)
+			print("train size:",Xt.shape,Tt.shape)
+			print("validate size:",Xv.shape,Tv.shape)			
+			W0,W1 = LeastSquare_SVD(Xt,Tt,0.8)
+			Yv =  LeastSquare_SVD_Eval(Xv,W0,W1)
+			Ev = Yv-Tv
+			E.extend(Ev)
+		E = numpy.asarray(E)
+		assert E.shape[0] == X.shape[0]
+		stdE = numpy.linalg.norm(E)
+		stdE = math.sqrt(stdE*stdE/E.shape[0])
+		print("standard deviation",stdE)
+		ES.append(stdE)
+	ES = numpy.asarray(ES)
+	numpy.savetxt("StandardDeviations"+".txt",ES)
+
+
+if __name__ == "__main__":
+	main()

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