U-net architectures for fast prediction of incompressible laminar flows

Understand

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.