Accurate modeling of surface pressure fields around objects is fundamental to aerodynamic analysis and design. While neural networks have shown promise as efficient alternatives to expensive Computational Fluid Dynamics (CFD) simulations, their applicability is often constrained by data scarcity and poor generalization across different aerodynamic domains. To address these challenges, we propose UniField, a unified framework that enables joint training across multiple aerodynamic domains including automobiles, trains, aircraft. UniField employs a shared geometry encoder to extract domain-agnostic representations from surface point clouds, and integrates domain-specific flow information through Parallel Flow-Conditioned Adaptive LayerNorm (PFC-AdaLN). In addition to consolidating existing datasets from specialized research field including automobiles, trains and aircraft, we further introduce ThingiCFD, a large-scale CFD dataset constructed from Thingi10k geometries with extensive flow condition randomization, substantially expanding geometric and flow diversity during training. UniField achieves SOTA performance on the public DrivAerNet++ benchmark. In addition, our experiments demonstrate that joint multi-domain training consistently improves surface pressure prediction accuracy, particularly in data-scarce domains. These results highlight the potential of UniField as a foundation model for data-driven aerodynamic modeling. Code and data will be available at https://github.com/zoujunhong/UniField.

翻译：精确建模物体周围的表面压力场是空气动力学分析与设计的基础。尽管神经网络已展现出作为昂贵计算流体动力学（CFD）模拟的高效替代方案的潜力，但其适用性常受限于数据稀缺性以及在不同空气动力学领域间的泛化能力不足。为解决这些挑战，我们提出UniField，一个统一的框架，支持跨多个空气动力学领域（包括汽车、列车、飞机）进行联合训练。UniField采用共享几何编码器从表面点云中提取领域无关的表征，并通过并行流场条件自适应层归一化（PFC-AdaLN）整合领域特定的流场信息。除了整合来自汽车、列车和飞机等专门研究领域的现有数据集，我们进一步引入了ThingiCFD——一个基于Thingi10k几何模型构建的大规模CFD数据集，该数据集通过广泛的流场条件随机化，显著扩展了训练过程中的几何与流场多样性。UniField在公开基准DrivAerNet++上取得了最先进的性能。此外，我们的实验表明，联合多域训练能够持续提升表面压力预测的准确性，尤其在数据稀缺的领域中。这些结果凸显了UniField作为数据驱动空气动力学建模基础模型的潜力。代码与数据将在https://github.com/zoujunhong/UniField 发布。