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.
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?
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:
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.
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!
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?
I'd strongly rephrase the title, this is NOT a book on physics-based deep learning.
This is a book on the deep learning approaches for physics problems DEVELOPED BY THIS RESEARCH GROUP. I think that is a very very important disclaimer to this book.
In addition, it is essentially used to strongly push their simulation framework Phi-Flow.
I would NOT call this an accurate depiction of the field.
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[ 2.9 ms ] story [ 77.2 ms ] thread> 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.
I would have called this one Deep Learning for Physics.
[1] https://ge.in.tum.de/
> 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...
Still a great topic though, no doubt!
https://www.stochasticlifestyle.com/the-essential-tools-of-s...
That said, it is a lovely set of topics.
No. I got the correct meaning at first glance.
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-...
https://news.ycombinator.com/item?id=28500577
Afaik, it's produced by Jupyter book[1], but find nothing in their docs either.
[1] https://jupyterbook.org/en/stable/intro.html
https://www.youtube.com/watch?v=yHiyJQdWBY8
This is a book on the deep learning approaches for physics problems DEVELOPED BY THIS RESEARCH GROUP. I think that is a very very important disclaimer to this book.
In addition, it is essentially used to strongly push their simulation framework Phi-Flow.
I would NOT call this an accurate depiction of the field.