3 ms·

"Physics-based" Deep Learning seems like a misnomer. From the abstract "Deep Learning Applications for Physics" sounds more apt. There definitely is value in tr

"Physics-based" Deep Learning seems like a misnomer. From the abstract "Deep Learning Applications for Physics" sounds more apt. There definitely is value in transferring standard terminology and methods from physics to deep learning. But from the preview it's unclear if that is the focus.

AbrahamParangi 5y ago
Indeed, “Deep Learning Based Physics” seems a little more correct.
- amcoastal 5y ago
  The common terminology is Physics Informed Neural Networks (PINNs)
  - ngcc_hk 5y ago
    The question of that is it target the network. But if it is for physics … should it be neural network using physics for physics (by physics scientist).
  - westurner 5y ago
    "Physics-informed neural networks" https://en.wikipedia.org/wiki/Physics-informed_neural_networks https://en.wikipedia.org/wiki/Physics-informed_neural_networ... But what about statistical thermodynamics and information theory? What about thin film? What are some applications for PINNs and for {DL, RL,} in physics?
nerdponx 5y ago
I definitely thought it was the opposite from the title, applying physics concepts/techniques to general deep learning problems.
omrjml 5y ago
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 5y ago
  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 5y ago
    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 5y ago
  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,...
deleted 5y ago
[deleted]

AbrahamParangi 5y ago

Indeed, “Deep Learning Based Physics” seems a little more correct.

amcoastal 5y ago
The common terminology is Physics Informed Neural Networks (PINNs)
- ngcc_hk 5y ago
  The question of that is it target the network. But if it is for physics … should it be neural network using physics for physics (by physics scientist).
- westurner 5y ago
  "Physics-informed neural networks" https://en.wikipedia.org/wiki/Physics-informed_neural_networks https://en.wikipedia.org/wiki/Physics-informed_neural_networ... But what about statistical thermodynamics and information theory? What about thin film? What are some applications for PINNs and for {DL, RL,} in physics?

nerdponx 5y ago

I definitely thought it was the opposite from the title, applying physics concepts/techniques to general deep learning problems.

omrjml 5y ago

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 5y ago
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 5y ago
  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 5y ago
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,...

deleted 5y ago

[deleted]