  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.
