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.
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.