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"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.
Does anyone know if I could approximate a closed form solution with a simple MLP network?
I was doing a project that simulate a projectile motion with drag and Magnus force. This has no analytical solutions and I currently solve it with a numeric solver. Does anyone know if I could approximate a closed form solution with a simple MLP network?
Earlier quoted context omitted.
Indeed, “Deep Learning Based Physics” seems a little more correct.
The common terminology is Physics Informed Neural Networks (PINNs)
But what about statistical thermodynamics and information theory? What about thin film?
What are some applications for PINNs and for {DL, RL,} in physics?
More like Deep Learning -based physics. Or more accurately: Deep Learning -assisted physics simulations.
Earlier quoted context omitted.
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.
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.
[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.]