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Whatever happened to that? Vapourware?
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?
Earlier quoted context omitted.
From a quick scan, I do not think they explicitly encode that. They want "the model to predict the evolution of diverse physical systems governed by partial differential equations". It looks like a more sophisticated sibling of time series forecasting models rather than a physics-informed nonparametric symbolic regression model.
Yeah, It’s true that PDEs are the "top-tier tool" for describing physical phenomena—from the laws of motion in classical mechanics and electromagnetic waves in electromagnetism to the evolution of wave functions in quantum mechanics, they accurately model most macroscopic, classical scenarios. However, when it comes to covering all physical phenomena, they really "fall short": in quantum gravity, spacetime may be dis…
GP was asking about conservation laws but in gravity you don't even have energy-momentum conservation.
> 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.
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.
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.
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…
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…
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.
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…
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.
Why? Is this important as a sanity check in the absence of any independent verifications?
I think one of the reasons it is important to preserve conservation laws is that, at the very least, you can be confident that your solution satisfies whatever physical laws your PDE relies on, even if it's almost certainly not the "actual" solution to the PDE. You actually can ensure that a numerical solver will approximately satisfy conservation laws. Then at the very least, even if your solution diverges from the "actual" PDEs solution, you can have some confidence that it's still a useful exploration of possible states. If conservation laws are not preserved AND your solution diverges from the "actual" PDE solution, then you probably cannot be confident about the model's utility.