Recent progress in \emph{Geometric Deep Learning} (GDL) has shown its potential to provide powerful data-driven models. This gives momentum to explore new methods for learning physical systems governed by \emph{Partial Differential Equations} (PDEs) from Graph-Mesh data. However, despite the efforts and recent achievements, several research directions remain unexplored and progress is still far from satisfying the physical requirements of real-world phenomena. One of the major impediments is the absence of benchmarking datasets and common physics evaluation protocols. In this paper, we propose a 2-D graph-mesh dataset to study the airflow over airfoils at high Reynolds regime (from $10^6$ and beyond). We also introduce metrics on the stress forces over the airfoil in order to evaluate GDL models on important physical quantities. Moreover, we provide extensive GDL baselines.
翻译:\emph{Geoprime Delearting} (GDL) (GDL) (GDL) 的近期进展表明,它有可能提供强大的数据驱动模型,这为探索从图形-Mesh数据中学习由图形-Mesh数据调控的物理系统的新方法提供了动力,然而,尽管做出了努力和最近取得的成就,一些研究方向仍未探索,进展仍然远远不能满足现实世界现象的物理要求。主要障碍之一是缺乏基准数据集和共同的物理评估协议。在本文中,我们提议建立一个二维图形模型数据集,用于研究雷诺兹高原系统的空气在空气中流(从10 6美元到10美元以上 6美元以上)的空气流。我们还引入了大气表面压力力指标,以便评估重要物理量的GDL模型。此外,我们提供了广泛的GDL基线。