[matthew_kuiash]

Uummm. Is this accelerated due to the inherent fuzziness of neural style calc or because they have to models working on NN/TF/GPU style hardware? I skimmed and couldn't find an answer. As for the "is it accurate?" question. No, no it isn't but then neither is any other model - it just has to be good enough over the time frames concerned. This is more like weather forecasting than chess.

[Y_Y]

From the article, it seems like what they're doing is equivalent to typical "super-resolution" tasks where you refine a coarse grid into a (consistent) fine one using AI heuristics. This one even has a GAN to make sure the difference between original and super-resolved data isn't easy to spot (which should be possible if the physics was wrong).

This is fine in physics generally, since the initial conditions aren't exact, often you just want some plausible result, rather than the one that corresponds exactly to the exact (microphysical) details of your input.

If someone else wants to actually read the paper and double check, that would be great.

[bawolff]

I think the question is less, is it accurate, and more do we know how inaccurate it is?

Inaccurate models csn still be useful as long as have an idea of how inaccurate they are in the worst case. If we have no idea how accurate something is, we can't tell if its sufficient for its purpose.