Factorized Implicit Global Convolution for Automotive Computational Fluid Dynamics Prediction

2025-02-06

AI Summary

To address the prohibitively high computational complexity (O(N³)) of CFD simulations on high-resolution 3D automotive geometries—hindering rapid aerodynamic performance prediction—this paper proposes Factorized Implicit Global Convolution (FIGConv), reducing complexity to O(N²). FIGConv introduces a novel factorized implicit grid and 2D reparameterized global convolution, enabling high-fidelity pressure-field estimation and drag prediction for arbitrary geometric inputs/outputs. Integrated within a U-Net architecture enhanced with 3D point-cloud geometric learning, the method achieves an R² of 0.95 for drag prediction on the DrivAerNet dataset, with 40% lower relative mean squared error (RMSE) and 70% lower absolute MSE compared to state-of-the-art methods. The core contribution lies in the first unified framework for vehicle aerodynamic modeling that jointly leverages implicit representation, factorized convolution, and geometry-adaptive reparameterization—all driven by CFD principles.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Deep Neural Architectures and Foundation Models
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

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.

Problem

• Efficiently solves CFD for large 3D meshes
• Reduces computational complexity to O(N^2)
• Improves drag prediction accuracy significantly

Innovation

• Factorized Implicit Global Convolution
• Quadratic complexity O(N^2)
• 2D reparameterization for global convolutions