Physics-Based Deep Learning Book

https://physicsbaseddeeplearning.org/intro.html

by Anon84 • 5 years ago

195 24 5 years ago

"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.

nerdponx2• 5 years ago

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

AbrahamParangi• 5 years ago

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

amcoastal• 5 years ago

The common terminology is Physics Informed Neural Networks (PINNs)

omrjml2• 5 years 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• 5 years 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.

jessriedel• 5 years 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,...

Archit3ch• 5 years ago

Why python? Doesn't Julia compose traditional ODE solvers with differential programming better?

UncleOxidant• 5 years ago

Found a couple of resources for what they call Physics-Informed Neural Networks in Julia:

https://mitmath.github.io/18337/lecture3/sciml.html

https://diffeqflux.sciml.ai/dev/

https://www.youtube.com/watch?v=HKJB0Bjo6tQ (Interpretable Deep Learning for Physics - I don't think there's any Julia in the video itself, but Miles Cranmer uses Julia for this work - he created SymbolicRegression.jl)

atty• 5 years ago

Because the python/numpy/tensorflow/Pytorch ecosystem is the deep learning ecosystem. You write a book on what you know, and what the audience wants. And everything they do here has tools that work just fine in python, no need to switch to Julia. And as someone who does this sort of work as my job, I can tell you the tool chains we use for what could be considered “industrial-scale” applications do not yet have appropriate Julia-based replacements.

amkkma• 5 years ago

That's because it's keeping things fairly simple. Implementing the more involved/advanced techniques would quickly outpace what's possible in python, now and for the foreseeable future

(see https://www.stochasticlifestyle.com/useful-algorithms-that-a... but also other reasons such as composability )

tubby12345• 5 years ago

is this the "why not rewrite it in rust" of the DL space?

nerdponx2• 5 years ago

Yes, pretty much. Not just deep learning, pretty much all data-related work. And, much like Rust, it's a good question, worth asking, and asking it repeatedly helps the world arrive at a more refined answer, and improves Julia in the process.

auntienomen• 5 years ago

None of the guts of these packages is written in Python anyways. C mostly, maybe with some Cython or Fortran. Python is where the API is presented, because that makes it easily accessible to lots of users.

s1291• 5 years ago

What a coincidence! just before I come across this post, I was reading this book this morning.

sampo• 5 years ago

More like Deep Learning -based physics. Or more accurately: Deep Learning -assisted physics simulations.

lambdatronics2• 5 years ago

Yeah, the Julia-language equivalent is referred to as "scientific machine learning" but I don't like that either. I think "simulation learning" would be snappier and maybe more appropriate.

ivan_ah• 5 years ago

meta comment: the website you're viewing when you click that link is generated using Jupyter Book , which is like the best thing ever https://jupyterbook.org/intro.html

Imagine all the beauty of .Rmd (easily generate books by combining markdown explanations with executable cells), but adapted for the Python ecosystem (jupyter notebooks).

Jupyuter-book is a really well thought-out project. You create the book with a _toc.yml file: https://github.com/tum-pbs/pbdl-book/blob/main/_toc.yml and all the config is in one file: https://github.com/tum-pbs/pbdl-book/blob/main/_config.yml (the build system leverages Sphinx which is the docs workhorse in the Python world)

One of the coolest things is the "Launcher" option which gives readers the options to "run" any notebook interactively (using the rocket button in the top right). It's a one-line config https://github.com/tum-pbs/pbdl-book/blob/main/_config.yml#L... A similar config would enable the "Launch in Pybinder" option which is a free ephemeral jupyter provider, see https://mybinder.org/

This "execute anywhere" option is nicely abstracted away as the `thiebe` library, and there is even POC work to run a pyodide kernel (https://github.com/executablebooks/thebe/issues/465) so soon all of this goodness will work offline in your browser!

As an educator, it's hard not to get excited about the future, given the pace at which learning/teaching tooling is developing!

ngcc_hk2• 5 years ago

Need another thread on this. It is a major learning tool when I first encountered Jupyter.

RavlaAlvar• 5 years ago

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?

omrjml2• 5 years ago

Not MLP, but PySindy[0] might be a way to find a closed form solution. A simple MLP may not be best way to achieve what are you are after.

[0] https://github.com/dynamicslab/pysindy

Physics-Based Deep Learning Book

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