Factorized Implicit Global Convolution for Automotive Computational Fluid Dynamics Prediction
This addresses efficiency challenges in automotive design CFD simulations, offering a domain-specific incremental improvement over prior methods.
The paper tackles the computational complexity of deep learning for automotive Computational Fluid Dynamics (CFD) on large 3D meshes by proposing Factorized Implicit Global Convolution (FIGConv), which reduces complexity from cubic to quadratic and achieves an R^2 of 0.95 for drag prediction with a 40% improvement in relative mean squared error.
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.