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.

翻译：精确的气动场预测对于车辆阻力评估至关重要，但高保真计算流体力学（CFD）的计算成本阻碍了其在迭代设计工作流程中的应用。尽管基于学习的方法能够实现快速且可扩展的推断，但精确建模气动场仍然具有挑战性，因为它需要同时捕捉长程几何效应和精细尺度的流动结构。现有方法通常仅在输入时对几何进行一次编码，并将预测表述为一次性映射，这往往导致全局形状感知能力被稀释，以及对尖锐局部流动变化的分辨率不足。为解决这些问题，我们提出了GA-Field，一个几何感知场预测网络，它引入了两个互补的设计组件：(i) 一个全局几何注入机制，该机制在网络的多个阶段重复地以紧凑的三维几何嵌入为条件，以保持长程几何一致性；(ii) 一个从粗到精的场细化策略，以恢复尖锐的局部气动细节。GA-Field在ShapeNet-Car和用于表面压力、壁面剪切应力以及三维速度预测任务的大规模DrivAerNet++基准测试中取得了新的最先进性能，同时在不同车辆类别间展现出强大的分布外泛化能力。