The surface pressure field of transportation systems, including cars, trains, and aircraft, is critical for aerodynamic analysis and design. In recent years, deep neural networks have emerged as promising and efficient methods for modeling surface pressure field, being alternatives to computationally expensive CFD simulations. Currently, large-scale public datasets are available for domains such as automotive aerodynamics. However, in many specialized areas, such as high-speed trains, data scarcity remains a fundamental challenge in aerodynamic modeling, severely limiting the effectiveness of standard neural network approaches. To address this limitation, we propose the Adaptive Field Learning Framework (AdaField), which pre-trains the model on public large-scale datasets to improve generalization in sub-domains with limited data. AdaField comprises two key components. First, we design the Semantic Aggregation Point Transformer (SAPT) as a high-performance backbone that efficiently handles large-scale point clouds for surface pressure prediction. Second, regarding the substantial differences in flow conditions and geometric scales across different aerodynamic subdomains, we propose Flow-Conditioned Adapter (FCA) and Physics-Informed Data Augmentation (PIDA). FCA enables the model to flexibly adapt to different flow conditions with a small set of trainable parameters, while PIDA expands the training data distribution to better cover variations in object scale and velocity. Our experiments show that AdaField achieves SOTA performance on the DrivAerNet++ dataset and can be effectively transferred to train and aircraft scenarios with minimal fine-tuning. These results highlight AdaField's potential as a generalizable and transferable solution for surface pressure field modeling, supporting efficient aerodynamic design across a wide range of transportation systems.

翻译：交通运输系统（包括汽车、列车与飞行器）的表面压力场对于气动分析与设计至关重要。近年来，深度神经网络已成为表面压力场建模中具有前景的高效方法，可作为计算成本高昂的CFD模拟的替代方案。当前，在汽车空气动力学等领域已存在大规模公开数据集。然而，在高速列车等诸多专业领域中，数据稀缺仍是气动建模面临的根本挑战，严重制约了标准神经网络方法的有效性。为应对这一局限，本文提出自适应场学习框架（AdaField），该框架通过在公开大规模数据集上进行预训练，以提升模型在数据有限子领域中的泛化能力。AdaField包含两个核心组件：首先，我们设计了语义聚合点变换器（SAPT）作为高性能主干网络，可高效处理大规模点云数据以实现表面压力预测；其次，针对不同气动子域间流场条件与几何尺度的显著差异，我们提出了流场条件适配器（FCA）与物理信息数据增强（PIDA）。FCA使模型能够通过少量可训练参数灵活适应不同流场条件，而PIDA则通过扩展训练数据分布以更好地覆盖物体尺度与速度的变化范围。实验表明，AdaField在DrivAerNet++数据集上取得了最先进的性能，并可通过极少量微调有效迁移至列车与飞行器场景。这些结果凸显了AdaField作为表面压力场建模的通用可迁移解决方案的潜力，能够为广泛交通运输系统的高效气动设计提供支持。