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Towards a Physics Foundation Model
21–30 of 32 posts
Re: Towards a Physics Foundation Model
#22Earlier quoted context omitted.
Why? Is this important as a sanity check in the absence of any independent verifications?
I'm not an expert on this, so take this with a grain of salt. Chaotic PDEs are extremely sensitive to initial conditions. This essentially makes it so that any numerical solution will (quickly) diverge from the true solution over time. (Just due to floating point error, discretization error, etc.) This is why for a lot of turbulent navier-stokes stuff, people don't necessarily care about the specific phenomena that o…
Actually I just happened to see this: https://www.stochasticlifestyle.com/how-chaotic-is-chaos-how....
It's basically explaining the same thing, but much better than me :-)
Re: Towards a Physics Foundation Model
#23Author here, 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…
Would you care to name any of the groups or papers you've had your eye on? Thanks!
Re: Towards a Physics Foundation Model
#24For folks wondering whether to read or not, here is the conclusion from the paper verbatim > 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 o…
Thanks, I haven't been able to give the paper a proper read, but are they're basing claims via results or the ability to recover physics equations?
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.
Re: Towards a Physics Foundation Model
#25Anyone remember that one time, a year or so ago, when some company teased a physics based generative model which showcased a drop of water sliding down a beer bottle and the model could display the forces acting on it? Whatever happened to that? Vapourware?
I think you mean this: https://genesis-embodied-ai.github.io/ It seems that this is much more focused on robotics, but interesting nonetheless.
Genesis is also a traditional physics engine, no ML-based physics prediction going on here. To my understanding their performance gains mainly come from building the engine to be highly parallelizable.
Re: Towards a Physics Foundation Model
#26Author here, 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…
Very interesting! In your internal testing, did you also compare your results with the transformer model from this paper: https://arxiv.org/abs/2506.17774 from July?
Re: Towards a Physics Foundation Model
#27Author here, 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…
Very interesting! In your internal testing, did you also compare your results with the transformer model from this paper: https://arxiv.org/abs/2506.17774 from July?
Very interesting paper! We did not run this model ourselves.
From what I've understood, the results are in the same order of magnitude, but the model is 4x the size. And (similar to all other predecessors), they finetune on new physics instead of zero-shot
Re: Towards a Physics Foundation Model
#28Author here, 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…
Some month ago i stumbled upon two arcticles discussing PINNs and their failiures in more complex settings. are there similar challenges?
Can you point me to the papers? In general, faster dynamics and chaotic systems are probably the hardest. Of course combined with long-term stability
Re: Towards a Physics Foundation Model
#29Author here, 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…
Would you care to name any of the groups or papers you've had your eye on? Thanks!
The Polymathic AI company does a lot of stuff in that direction.
Re: Towards a Physics Foundation Model
#30Author here, 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…
Great paper! 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 t…
I haven't seen electromagnetic systems included yet, probably since they are less training data for it.
In your chase, with such specific systems, a model trained only on your data might make more sense, though