提出新型神经网络，加速汽车流体模拟，处理超大三维网格
Factorized Implicit Global Convolution for Automotive Computational Fluid Dynamics Prediction
- 分解隐式网格+二维重参数化，实现二次复杂度计算
- 在DrivAerNet数据集上拖拽力预测R²达0.95，误差降低70%
- 适合需要高效高精度流体仿真的汽车设计工程师
计算流体力学（CFD）对汽车设计至关重要，需分析大型3D点云以研究车体形状对压力场和阻力的影响。但现有深度学习方法在处理高分辨率3D数据时面临巨大计算负担。本文提出因子化隐式全局卷积（FIGConv），一种新颖架构，可高效求解具有任意输入输出几何形状的大型3D网格流体问题。该方法将复杂度降至二次项$O(N^2)$，显著优于传统3D神经网络模型的三次项$O(N^3)$。其结合因子化隐式网格近似高分辨率域、通过二维重参数化实现高效全局卷积，并采用U型结构有效整合信息。我们在行业标准的Ahmed body数据集和大规模DrivAerNet数据集上验证方法。在DrivAerNet中，模型拖拽力预测的$R^2$值达0.95，显著超越此前最先进方法：相对均方误差降低40%，绝对均方误差降低70%。
原文摘要 · Abstract (English)
Computational Fluid Dynamics (CFD) is crucial for automotive design, requiring the analysis of large 3D point clouds to study how vehicle geometry affects pressure fields and drag forces. However, existing deep learning approaches for CFD struggle with the computational complexity of processing high-resolution 3D data. We propose Factorized Implicit Global Convolution (FIGConv), a novel architecture that efficiently solves CFD problems for very large 3D meshes with arbitrary input and output geometries. FIGConv achieves quadratic complexity $O(N^2)$, a significant improvement over existing 3D neural CFD models that require cubic complexity $O(N^3)$. Our approach combines Factorized Implicit Grids to approximate high-resolution domains, efficient global convolutions through 2D reparameterization, and a U-shaped architecture for effective information gathering and integration. We validate our approach on the industry-standard Ahmed body dataset and the large-scale DrivAerNet dataset. In DrivAerNet, our model achieves an $R^2$ value of 0.95 for drag prediction, outperforming the previous state-of-the-art by a significant margin. This represents a 40% improvement in relative mean squared error and a 70% improvement in absolute mean squared error over previous methods.
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