The article initially appears to suggest that all AI in science (or at least the author’s field) is hype. But their gripe seems to be specific to an architecture named PINN that seems to be overhyped, as they mention in the end how they end up using other DL models to successfully compute PDEs faster than traditional numerical methods.
It's more widespread than PINNs. PINNs have been widely known to be rubbish a long time ago. But the general failure of using ML for physics problems is much more widespread.
Where ML generally shines is either when you have relatively lots of experimental data with respect to a fairly narrow domain. This is the case for machine learned interatomic potentials MLIPs which have been a thing since the '90s. Also potentially the case for weather modelling (but I do not want to comment about that). Or when you have absolute insane amounts of data, and you train a really huge model. This is what we refer to as AI. This is basically why Alphafold is successful, and Alphafold still fails to produce good results when you query it on inputs that are far from any data points in its training data.
But most ML for physics problems tend to be somewhere in between. Lacking experimental data and working with not enough simulation data because it is so expensive to produce. And also training models that are not large enough, because inference would be too slow, anyway, if they were too big. And then expecting these models to learn a very wide range of physics.
And then everyone jumps in on the hype train, because it is so easy to give it a shot. And everyone gets the same dud results. But then they publish anyway. And if the lab/PI is famous enough or if they formulate the problem in a way that is unique and looks sciency or mathy, they might even get their paper in a good journal/conference and get lots of citations. But in the end, they still only end up with the same results as everyone else: replicates the training data to some extent, somebody else should work on the generalizability problem.
The use of the term «AI» is, yet again, annoying by its vagueness.
I'm assuming that they do not refer to the general use of machines to solve differential equations (whether exactly or approximately), which is centuries old (Babbage's engine).
But then how restricted these «Physics-Informed Neural Networks» are ? Are there other methods using Neural Networks to solve differential equations ?
Replace PINN with any "AI" solution for anything and you'll still find it overhyped.
The only realistic evaluations of "AI" so far are those that admit it's only useful for experts to skip some boring work. And triple check the output after.
