[HN Gopher] Physics-Based Deep Learning Book
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Physics-Based Deep Learning Book
Author : sebg
Score : 294 points
Date : 2024-07-11 22:10 UTC (1 days ago)
(HTM) web link (physicsbaseddeeplearning.org)
(TXT) w3m dump (physicsbaseddeeplearning.org)
| jessriedel wrote:
| TBC, this is about deep learning for physics problems, not a
| general approach to deep learning from a physicist's perspective.
|
| > This document contains a practical and comprehensive
| introduction of everything related to deep learning in the
| context of physical simulations. As much as possible, all topics
| come with hands-on code examples in the form of Jupyter notebooks
| to quickly get started. Beyond standard supervised learning from
| data, we'll look at physical loss constraints, more tightly
| coupled learning algorithms with differentiable simulations,
| training algorithms tailored to physics problems, as well as
| reinforcement learning and uncertainty modeling. We live in
| exciting times: these methods have a huge potential to
| fundamentally change what computer simulations can achieve.
| esafak wrote:
| That would have been the more interesting book. There is a lot
| of intuition that statistical mechanics could bring to deep
| learning.
|
| I would have called this one _Deep Learning for Physics_.
| beefok wrote:
| I guess considering their group is called the Physics-based
| Simulation Group [1], I'm thinking maybe that's just the
| terminology they've always used? Or maybe it's a
| German->English translation thing?
|
| [1] https://ge.in.tum.de/
| ayhanfuat wrote:
| I think the other one is more commonly known as "physics
| informed deep learning".
| jessriedel wrote:
| I don't think so? Wikipedia suggests that "Physics-informed
| neural networks" is
|
| > a type of universal function approximators that can embed
| the knowledge of any physical laws that govern a given data-
| set in the learning process
|
| https://en.wikipedia.org/wiki/Physics-
| informed_neural_networ...
|
| In other words, that seems to refer to giving the model prior
| info (a bias) about physical laws that generated the data.
| What I'm talking about is more abstract: using physics-y type
| math ideas to understand the internal behavior of the
| networks. Here are a couple examples:
|
| https://proceedings.neurips.cc/paper_files/paper/2023/hash/6.
| ..
|
| https://cgad.ski/blog/where-is-noethers-principle-in-
| machine...
| richard___ wrote:
| The most important question - how to apply these methods to
| contact dynamics?
| richrichie wrote:
| Chris has done good work on this genre. His differential
| equations Julia package with support for physics or sci ML is
| pretty cool.
|
| https://www.stochasticlifestyle.com/the-essential-tools-of-s...
| danielmarkbruce wrote:
| Hopefully this is a great book, what a great topic to write a
| book about. Kudos to the author.
| sriram_malhar wrote:
| The title is misleading, no? It seems to be about how to apply
| deep learning to physics simulations. It is not about borrowing
| physics concepts and applying them to the NN landscape.
|
| That said, it is a lovely set of topics.
| 77pt77 wrote:
| > The title is misleading, no?
|
| No. I got the correct meaning at first glance.
| makerofthings wrote:
| How many IBM Technical Support workers does it take to change
| a lightbulb? None, we have an identical model here and ours
| is working fine.
| danielmarkbruce wrote:
| It is misleading. The is not DL based on physics. It is physics
| based on DL.
| __rito__ wrote:
| Some other good resources-
|
| 1. CRUNCH group YouTube (talks on Math + ML) -
| https://m.youtube.com/channel/UC2ZZB80udkRvWQ4N3a8DOKQ
|
| 2. Steve Brunton's Physics Informed Machine Learning playlist -
| https://m.youtube.com/playlist?list=PLMrJAkhIeNNQ0BaKuBKY43k...
|
| 3. The book "Data Driven Science and Engineering" from Steve
| Brunton
|
| 4. Deep Learning in Scientific Computation from ETH Zurich -
| https://m.youtube.com/playlist?list=PLJkYEExhe7rYY5HjpIJbgo-...
| croemer wrote:
| Previous discussion in 2021:
|
| https://news.ycombinator.com/item?id=28500577
| fragebogen wrote:
| Maybe I'm blind, but how do I download the entire book as PDF? I
| only find the download button up top for individual pages?
|
| Afaik, it's produced by Jupyter book[1], but find nothing in
| their docs either.
|
| [1] https://jupyterbook.org/en/stable/intro.html
| eru wrote:
| See https://github.com/tum-pbs/pbdl-book/issues/1
| privong wrote:
| Direct link to the arXiv abstract page, where one can
| download the PDF: https://arxiv.org/abs/2109.05237
| Xeyz0r wrote:
| Sounds like a valuable resource for both beginners and
| experienced
| alexb24 wrote:
| In this dense overview presentation (Oct 2022), Chris Rackauckas
| introduced Sci ML with diverse examples from many fields:
| epidemics, gravitational waves, pharmacometrics, ocean
| simulation... and some open source and proprietary Julia
| libraries for SciML. Highly informative!
|
| https://www.youtube.com/watch?v=yHiyJQdWBY8
| rmbyrro wrote:
| Does anyone know what the job market looks like for a "Physics-
| simulation ML engineer" (or whatever it's called)?
| joelthelion wrote:
| I was wondering : does deep learning have the potential to make
| large-scale quantum physics simulations more tractable? How about
| plasma physics for fusion reactors?
| danielmarkbruce wrote:
| It obviously has the _potential_.... work in weather and
| biology points to it.
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