I think this article does a good job of highlighting the difference between simulations and ML-based approaches. The latter are faster, but have limitations out

I think this article does a good job of highlighting the difference between simulations and ML-based approaches. The latter are faster, but have limitations outside of their training parameters. As with everything in ML, broader training data to cover those cases probably helps. Though I would guess some of the problems could be inherent, that there fundamentally is no computational shortcut to this problem, whether you use a neural network or not.