Surrogate models are necessary to optimize meaningful quantities in physical dynamics as their recursive numerical resolutions are often prohibitively expensive. It is mainly the case for fluid dynamics and the resolution of Navier-Stokes equations. However, despite the fast-growing field of data-driven models for physical systems, reference datasets representing real-world phenomena are lacking. In this work, we develop AirfRANS, a dataset for studying the two-dimensional incompressible steady-state Reynolds-Averaged Navier-Stokes equations over airfoils at a subsonic regime and for different angles of attacks. We also introduce metrics on the stress forces at the surface of geometries and visualization of boundary layers to assess the capabilities of models to accurately predict the meaningful information of the problem. Finally, we propose deep learning baselines on four machine learning tasks to study AirfRANS under different constraints for generalization considerations: big and scarce data regime, Reynolds number, and angle of attack extrapolation.

翻译：替代模型对于优化物理动力学中的有意义的量是必要的，因为其递归数值求解通常代价高昂。这在流体动力学以及纳维-斯托克斯方程的求解中尤为突出。然而，尽管面向物理系统的数据驱动模型领域发展迅速，但代表真实世界现象的参考数据集仍然匮乏。在本工作中，我们开发了AirfRANS数据集，用于研究亚音速条件下、不同攻角下翼型上的二维不可压缩稳态雷诺平均纳维-斯托克斯方程。我们还引入了几何表面应力力的度量以及边界层可视化方法，以评估模型精确预测问题中关键信息的能力。最后，我们提出了四项机器学习任务上的深度学习基线，用于在不同约束下研究AirfRANS的泛化特性：大数据与稀疏数据场景、雷诺数以及攻角外推。