Learning the solution operator of two-dimensional incompressible Navier-Stokes equations using physics-aware convolutional neural networks

Understand

In recent years, the concept of introducing physics to machine learning has become widely popular.

• Most physics-inclusive ML-techniques however are still limited to a single geometry or a set of parametrizable geometries.
• Thus, there remains the need to train a new model for a new geometry, even if it is only slightly modified.
• With this work we introduce a technique with which it is possible to learn approximate solutions to the steady-state Navier--Stokes equations in varying geometries without the need of parametrization.