4/22/2025 at 8:20:33 PM
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.by grunder_advice
4/22/2025 at 9:47:04 PM
I think an important question to ask is whether your scientific task is primarily one of interpolation, or one of extrapolation. LLMs appear to be excellent interpolators. They are bad at extrapolation.by raddan
4/22/2025 at 9:51:32 PM
Climate models aren't LLMs.by immibis
4/23/2025 at 8:35:18 AM
All current neural network based climate models use some kind of transformer architecture. So in that sense they are at least related to LLMs.by mnky9800n
4/22/2025 at 10:58:33 PM
They're also not AI.It remains to be seen exactly how much a climate model can be improved by AI. They're already based on woefully sparse data points.
by da_chicken
4/23/2025 at 12:48:43 AM
Why are they not AI?by philipwhiuk
4/23/2025 at 8:34:12 AM
All of the ai climate models, such as the one by Microsoft mentioned, are trained on model outputs not data. They simply are learning the model that was already developed. The same is true for the Google model which is trained on era5 reanalysis which is a mix of modeled and real data. From my perspective the main benefits of these neural network replacements is that they can, once trained, run on desktop hardware and are much less onerous to implement and iterate with. Their power lies in their ability to be used easily and efficiently, not in that they are able to discern physics from raw data.by mnky9800n
4/23/2025 at 4:14:56 AM
I find it interesting that fluid dynamics models struggle more than weather. Intuitively, my view of weather is that it looks a lot like fluid dynamics and seems quite chaotic. Is this wrong or is it just the vast amount of recorded weather data which helps?edit: I realised just after posting that you only mentioned climate models. It was another poster who said there were successes using ML for meteorology.
by Agentlien
4/22/2025 at 9:15:46 PM
Check out Fourier Neural Operators, they claim to have a pretty solid solver for fluid flow equations (Navier Stokes etc).by season2episode3
4/22/2025 at 9:32:57 PM
I am already acquainted with them but to be honest, I am no longer in the field so I am not able to comment on latest developments. However, as of two years ago, the consistent result was that you could get models that reproduce really good physics for problems in the same physical regimes as the training data, but such models had poor generalizability, so depending on the use case, they weren't of much use. The only exception I know is FourCastNet, which is a weather model FNO from NVIDIA.by grunder_advice