Towards a Physics Foundation Model

Post by flwi » Fri, Sep 19, 2025, 9:16 AM UTC

godelski wrote: post #24

witnessme wrote: post #14

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 o…

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) wit…

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!

Post by godelski » Fri, Sep 19, 2025, 5:00 PM UTC

flwi wrote: post #31

godelski wrote: post #24

Earlier quoted context omitted.

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) wit…

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…

  > 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?

Re: Towards a Physics Foundation Model

Re: Towards a Physics Foundation Model