Define "fundamental", but I added skills to run complicated evaluation procedures for my ML research. This way I can open 5 CC instances, let them run and iterate on research without intervention. After that, I can do the final review.
Hey author here. Your argument is completely valid, we only model physics implicitly and thus have no prove that the model "actually knows the physics". Practically, this might not matter much: If the model can predict the evolution of the system to a certain accuracy, the user won't care about the underlying knowledge. And even for modern physics (quantum / GR), we know we miss something and yet, the models we have are incredibly useful.
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
It sounds like you didn't actually read what I wrote then
> the user won't care about the underlying knowledge.
I hear this argument a lot and it's tiresome. No one here is not concerned with results. Why imply that's not my main concern?
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
We or you? Those are very different things. Is it a black box because you can't look inside out because you didn't look inside? Because I think you'll find some works that do exactly what we're talking about here. And if you're going to make big talk about PINNs then you need to know their actual purpose. Like come on man, you're claiming a physics model. How can you claim a physics model without the physics?
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.
Author here: we do NOT do conservation of energy/momentum. We are currently trying to incorporate that in a follow up paper, but for now, the models that try that (e.g. PINNs (soft constraint) or hard constraint models, all perform badly when modeling multiple systems.
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
Author here: we do NOT do conservation of energy/momentum. We are currently trying to incorporate that in a follow up paper, but for now, the models that try that (e.g. PINNs (soft constraint) or hard constraint models, all perform bad when modeling multiple systems.
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
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.
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
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?
I think it is the general attractiveness of para social relationships (https://en.m.wikipedia.org/wiki/Parasocial_interaction). People look for personal, intimate interactions.
OF creates (the illusion of) such relationships.
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