HN Reader

Author 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 significant progress here.

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.

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?

Whatever happened to that? Vapourware?

Not the "foundational model" of physics I was expecting, but this is still great to see!

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.

ITAR will be quite a surprise to some when it suddenly makes an appearance.