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.

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 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.

[1] https://ieeexplore.ieee.org/abstract/document/6248685

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?

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?

Some month ago i stumbled upon two arcticles discussing PINNs and their failiures in more complex settings. are there similar challenges?

Would you care to name any of the groups or papers you've had your eye on? Thanks!