Totally newbie question - 'approximating very complicated behavior' - this seems like a perfect problem for ML to me. Is this something that's used or explored ?
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.