  Machine learning is a popular tool that is being applied to many domains,
from computer vision to natural language processing. It is not long ago that
its use was extended to physics, but its capabilities remain to be accurately
contoured. In this paper, we are interested in the prediction of 2D velocity
and pressure fields around arbitrary shapes in laminar flows using supervised
neural networks. To this end, a dataset composed of random shapes is built
using Bezier curves, each shape being labeled with its pressure and velocity
fields by solving Navier-Stokes equations using a CFD solver. Then, several
U-net architectures are trained on the latter dataset, and their predictive
efficiency is assessed on unseen shapes, using ad hoc error functions.
