Accurate machine-learning models for aerodynamic prediction are essential for accelerating shape optimization, yet remain challenging to develop for complex three-dimensional configurations due to the high cost of generating training data. This work introduces a methodology for efficiently constructing accurate surrogate models for design purposes by first pre-training a large-scale model on diverse geometries and then fine-tuning it with a few more detailed task-specific samples. A Transformer-based architecture, AeroTransformer, is developed and tailored for large-scale training to learn aerodynamics. The methodology is evaluated on transonic wings, where the model is pre-trained on SuperWing, a dataset of nearly 30000 samples with broad geometric diversity, and subsequently fine-tuned to handle specific wing shapes perturbed from the Common Research Model. Results show that, with 450 task-specific samples, the proposed methodology achieves 0.36% error on surface-flow prediction, reducing 84.2% compared to training from scratch. The influence of model configurations and training strategies is also systematically studied to provide guidance on effectively training and deploying such models under limited data and computational budgets. To facilitate reuse, we release the datasets and the pre-trained models at https://github.com/tum-pbs/AeroTransformer. An interactive design tool is also built on the pre-trained model and is available online at https://webwing.pbs.cit.tum.de.

翻译：准确的气动预测机器学习模型对于加速形状优化至关重要，但由于生成训练数据成本高昂，针对复杂三维构型开发此类模型仍然具有挑战性。本文提出了一种高效构建设计用精确代理模型的方法，该方法首先在大规模多样化几何形状上预训练一个大型模型，然后使用少量更详细的任务特定样本进行微调。我们开发并定制了一个基于Transformer的架构AeroTransformer，用于大规模训练以学习气动特性。该方法在跨声速机翼上进行了评估，模型在SuperWing数据集（包含近30000个具有广泛几何多样性的样本）上进行预训练，随后微调以处理从通用研究模型（Common Research Model）扰动的特定机翼形状。结果表明，使用450个任务特定样本，所提出的方法在表面流动预测上达到0.36%的误差，相比从头训练降低了84.2%。本文还系统研究了模型配置和训练策略的影响，为在有限数据和计算预算下有效训练和部署此类模型提供指导。为便于复用，我们在https://github.com/tum-pbs/AeroTransformer 发布了数据集和预训练模型，并基于预训练模型构建了交互式设计工具，可通过https://webwing.pbs.cit.tum.de 在线访问。