It's absolutely being explored. There is a lot of active research into using ML to learn solutions of PDEs (Navier-Stokes in this case). It's not my field so I don't know much about the specifics.
The works that I've read train an NN on numerical solutions for different geometries and boundary conditions. Then they try to infer the solutions for configurations outside the training set, which should be much faster than recomputing the numerical solution.
The works that I've read train an NN on numerical solutions for different geometries and boundary conditions. Then they try to infer the solutions for configurations outside the training set, which should be much faster than recomputing the numerical solution.