Earlier quoted context omitted.
I don't understand why this is downvoted. This is a classic thing to do with deep learning: take something that has a solution that is expensive to compute, and then train a deep learning model from that. And along the way, your model might yield improvements, too, and you can layer in additional features, interpolate at finer-grained resolution, etc. If nothing else, the forward pass in a deep learning model is almo…
> the forward pass in a deep learning model is almost certainly way faster than simulating the next step in a numerical simulation Is this the case in most of such refinements (architecture wise)?
Practically speaking yes. You'd not likely build a statistical model when you could build a good simulation of the underlying process if the simulation was already really fast and accurate.