Towards a Physics Foundation Model
arxiv.org
arxiv.org
Wow, I didn't think this would HN. I actually planned to do the advertisement rounds only after the final ICLR submission.
This is our attempt at creating a model which understands multiple physics, which is in contrast to PINNs and Neural Operators, which focus on much more narrow systems.
Obviously, the biggest issue is still data (3D and real-world problems), but I think we and a few other groups make significant progress here.
Off the top of your head, are you aware of any similar general-multiphysics NN work that's been applied to electromagnetics problems? In particular, some colleagues in my lab are investigating imaging via acoustic waves which are induced by microwave absorptive heating (in liquids, biological tissues, etc.); this approach is most commonly known as RF-induced thermoacoustic imaging [1]. It's very tricky to model this phenomenon in simulation, doubly so to measure it experimentally.
Most in my lab (myself included) are leery of throwing NNs at problems and seeing what sticks, but sometimes I wonder whether a model like yours might help us skip past the boring details to get at the novel technical stuff, or else extend simulations to more complicated boundary conditions.
In your chase, with such specific systems, a model trained only on your data might make more sense, though
> We have demonstrated that a single transformer-based model can effectively learn and predict the dynamics of diverse physical systems without explicit physics-specific features, marking a significant step toward true Physics Foundation Models. GPhyT not only outperforms specialized architectures on known physics by up to an order of magnitude but, more importantly, exhibits emergent in-context learning capabilities—inferring new boundary conditions and even entirely novel physical phenomena from input prompts alone.
Because those two things are very different. You can have models that make accurate predictions without having accurate models of "the world" (your environment, not necessarily the actual world)[0]. We can't meaningful call something a physics model (or a world model) without that counterfactual recovery (you don't need the exact laws of physics but you need something reasonable). After all, our physics equations are the most compressed forms or representing the information we're after.
I ask because this is a weird thing that happens in a lot of ML papers when approaching world models. But just looking at results isn't enough to conclude if a world is being modeled. Doesn't even tell you if that's self consistent, let alone counterfactual.
[0] classic example is the geocentric model. They made accurate predictions, which is why it stayed around for so long. It's not like the heliocentric model didn't present new problems. There was reason for legitimate scientific debate at the time but that context is easily lost to history.
On a tangent, we cannot prove that LLMs actually know language, yet they can be incredibly useful. Of course, a true world model would be much nicer to have, I agree with that!
> Practically, this might not matter much: If the model can predict the evolution of the system to a certain accuracyI'm
> the user won't care about the underlying knowledge.
Read my example. People will care if you have a more complicated geocentric model. Geocentric was quite useful, but also quite wrong, distracting, and made many bad predictions as well as good ones.
The point is that it is wrong and this always bounds your model to being wrong. The big difference is if you don't extract the rules your model derived then you won't know when or how your model is wrong.
So yes, the user cares. Because the user cares about the results. This is all about the results...
> we cannot prove that LLMs actually know language
Whatever happened to that? Vapourware?
Perhaps, we will encounter the bitter lesson again and a well trained model will solve this. But as I said, we are also looking at hybrid models
Perhaps, we will encounter the bitter lesson again and a well trained model will solve this. But as I said, we are also looking at hybrid models
GP was asking about conservation laws but in gravity you don't even have energy-momentum conservation.
I think one of the reasons it is important to preserve conservation laws is that, at the very least, you can be confident that your solution satisfies whatever physical laws your PDE relies on, even if it's almost certainly not the "actual" solution to the PDE. You actually can ensure that a numerical solver will approximately satisfy conservation laws. Then at the very least, even if your solution diverges from the "actual" PDEs solution, you can have some confidence that it's still a useful exploration of possible states. If conservation laws are not preserved AND your solution diverges from the "actual" PDE solution, then you probably cannot be confident about the model's utility.