[omrjml]

This does not appear to be the usual approach of training the neural network on tons of data from the physics simulation. Instead they use the actual physics equations to form the loss function, which is a far more robust way of creating such an emulator. So physics based deep learning title is appropriate.

[kkylin]

Indeed. I see where everyone on HN is coming from. But if you're a physicist, and you've come across a lot of "deep learning applied to physics but the model has no physics in it" (and there's plenty of that), then the title may make perfect sense.

[lambdatronics]

Yeah, many scientists in my field have been justifiably skeptical of black-box machine/deep learning applications -- just sounded like the latest meaningless buzzword. I think this approach potentially is a big deal.

[Edit: by "this approach" I mean what the article is calling "differentiable physics" -- but I don't love that moniker. The "physics informed neural network" approach doesn't seem that great to me. It's much slower than doing an actual simulation, the resulting errors are larger, and you can't re-use results -- it's a one-off solution. The fact that you can use it to interpolate isn't that much of a selling point. The only nice thing is that you can throw any system of equations you want at it without having to design a numerical solver.]

[jessriedel]

Eh, they may put more emphasis on that technique, but its only a subset of the scope:

> This document contains a practical and comprehensive introduction of everything related to deep learning in the context of physical simulations. ... Beyond standard supervised learning from data, we’ll look at physical loss constraints, more tightly coupled learning algorithms with differentiable simulations,...