Data-driven generative models have emerged as promising approaches towards achieving efficient mechanical inverse design. However, due to prohibitively high cost in time and money, there is still lack of open-source and large-scale benchmarks in this field. It is mainly the case for airfoil inverse design, which requires to generate and edit diverse geometric-qualified and aerodynamic-qualified airfoils following the multimodal instructions, \emph{i.e.,} dragging points and physical parameters. This paper presents the open-source endeavors in airfoil inverse design, \emph{AFBench}, including a large-scale dataset with 200 thousand airfoils and high-quality aerodynamic and geometric labels, two novel and practical airfoil inverse design tasks, \emph{i.e.,} conditional generation on multimodal physical parameters, controllable editing, and comprehensive metrics to evaluate various existing airfoil inverse design methods. Our aim is to establish \emph{AFBench} as an ecosystem for training and evaluating airfoil inverse design methods, with a specific focus on data-driven controllable inverse design models by multimodal instructions capable of bridging the gap between ideas and execution, the academic research and industrial applications. We have provided baseline models, comprehensive experimental observations, and analysis to accelerate future research. Our baseline model is trained on an RTX 3090 GPU within 16 hours. The codebase, datasets and benchmarks will be available at \url{https://hitcslj.github.io/afbench/}.

翻译：数据驱动的生成模型已成为实现高效机械逆向设计的有前景的方法。然而，由于时间和金钱成本过高，该领域仍缺乏开源的大规模基准测试。翼型逆向设计尤其如此，它需要根据多模态指令（即拖拽点和物理参数）生成和编辑具有多样化几何质量与气动质量的翼型。本文介绍了在翼型逆向设计方面的开源工作，即AFBench，包括一个包含20万个翼型及高质量气动与几何标签的大规模数据集、两项新颖且实用的翼型逆向设计任务（即基于多模态物理参数的条件生成与可控编辑），以及用于评估现有各类翼型逆向设计方法的综合指标。我们的目标是将AFBench建立为一个用于训练和评估翼型逆向设计方法的生态系统，特别关注能够通过多模态指令弥合想法与执行、学术研究与工业应用之间差距的数据驱动可控逆向设计模型。我们提供了基线模型、全面的实验观察与分析，以加速未来的研究。我们的基线模型在RTX 3090 GPU上训练耗时16小时。代码库、数据集和基准测试将在 \url{https://hitcslj.github.io/afbench/} 提供。