[HN Gopher] Physics-Based Deep Learning Book
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Physics-Based Deep Learning Book
Author : Anon84
Score : 149 points
Date : 2021-09-12 12:36 UTC (10 hours ago)
(HTM) web link (physicsbaseddeeplearning.org)
(TXT) w3m dump (physicsbaseddeeplearning.org)
| ivan_ah wrote:
| 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!
| Archit3ch wrote:
| Why python? Doesn't Julia compose traditional ODE solvers with
| differential programming better?
| UncleOxidant wrote:
| 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 wrote:
| 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 wrote:
| 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 wrote:
| is this the "why not rewrite it in rust" of the DL space?
| nerdponx wrote:
| 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.
| sampo wrote:
| More like Deep Learning -based physics. Or more accurately: Deep
| Learning -assisted physics simulations.
| s1291 wrote:
| What a coincidence! just before I come across this post, I was
| reading this book this morning.
| gillesjacobs wrote:
| "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.
| nerdponx wrote:
| I definitely thought it was the opposite from the title,
| applying physics concepts/techniques to general deep learning
| problems.
| omrjml wrote:
| 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.
| jessriedel wrote:
| 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,...
| kkylin wrote:
| 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.
| AbrahamParangi wrote:
| Indeed, "Deep Learning Based Physics" seems a little more
| correct.
| amcoastal wrote:
| The common terminology is Physics Informed Neural Networks
| (PINNs)
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(page generated 2021-09-12 23:00 UTC)