How do they prove their model preserves conservation principles? I looked in the paper & didn't find any evidence of how they verify that whatever their "trained" model is doing is actually physically plausible & maintains the relevant invariants like mass, energy, momentum, etc.
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.
What do you think about the Nobel prize in physics going for neural networks last year? What combinations of AI + physics do you think will be most impactful and could potentially get a Nobel prize?
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?
Anyone 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?
For 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 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.
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.
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[ 0.24 ms ] story [ 42.1 ms ] threadWow, 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.
Whatever happened to that? Vapourware?
> 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.