GA-Field: Geometry-Aware Vehicle Aerodynamic Field Prediction

TL;DR

GA-Field is a novel geometry-aware neural network that improves vehicle aerodynamic field predictions by incorporating global shape information repeatedly and refining local details, achieving state-of-the-art results efficiently.

Contribution

The paper introduces GA-Field, a new method that enhances aerodynamic predictions by integrating repeated global geometry conditioning and a coarse-to-fine refinement strategy.

Findings

Achieves state-of-the-art performance on ShapeNet-Car and DrivAerNet++ benchmarks.

Demonstrates strong out-of-distribution generalization across vehicle categories.

Effectively captures both global geometric effects and local flow details.

Abstract

Accurate aerodynamic field prediction is crucial for vehicle drag evaluation, but the computational cost of high-fidelity CFD hinders its use in iterative design workflows. While learning-based methods enable fast and scalable inference, accurately aerodynamic fields modeling remains challenging, as it demands capturing both long-range geometric effects and fine-scale flow structures. Existing approaches typically encode geometry only once at the input and formulate prediction as a one-shot mapping, which often leads to diluted global shape awareness and insufficient resolution of sharp local flow variations. To address these issues, we propose GA-Field, a Geometry-Aware Field prediction network that introduces two complementary design components: (i) a global geometry injection mechanism that repeatedly conditions the network on a compact 3D geometry embedding at multiple stages to preserve long-range geometric consistency, and (ii) a coarse-to-fine field refinement strategy to recover sharp local aerodynamic details. GA-Field achieves new state-of-the-art performance on ShapeNet-Car and the large-scale DrivAerNet++ benchmark for surface pressure, wall shear stress, and 3D velocity prediction tasks, while exhibiting strong out-of-distribution generalization across different vehicle categories.