Comment by grunder_advice

Is this a recruiting attempt by Los Alamos? AI/ML for science as this broad field used to be known is interesting. Some five years ago there as a real craze where every STEM lab at my university was doing some form of ML project. I think by now people have learned what works and what doesn't. Climate models for example have been quite successful. Possibly the reason is that they learn directly from collected data, rather than trying to emulate the output of simulations. Attempts to build similiar models for fluid dynamics have been rather dismal. In general, big models and big data result in useful models, even if only because these models seem to be somehow interpolating based on similiar training data points. Trying to replace classical physics based models with ML models trained on simulation data does not seem to work. The model is only ever capable of emulating a physically plausible output when the input is close enough to the training data, and that too, only when the system isn't chaotic. For applications where you are generating a sample to be used in a downstream task, ML models trained on lots of data can be very useful. You only need a few lucky guesses, that you can verify downstream, to end up with some useful result. In short, there is no magic to it. It's a useful tool that can be regarded as both a search algorithm and an optimization algorithm.