Physics-Based Deep Learning Book
physicsbaseddeeplearning.org
Physics-Based Deep Learning Book
1–10 of 26 posts
Re: Physics-Based Deep Learning Book
#2"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.
Re: Physics-Based Deep Learning Book
#3Why python? Doesn't Julia compose traditional ODE solvers with differential programming better?
Re: Physics-Based Deep Learning Book
#4Why python? Doesn't Julia compose traditional ODE solvers with differential programming better?
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)
Re: Physics-Based Deep Learning Book
#5"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.
I definitely thought it was the opposite from the title, applying physics concepts/techniques to general deep learning problems.
Re: Physics-Based Deep Learning Book
#6Why python? Doesn't Julia compose traditional ODE solvers with differential programming better?
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.
Re: Physics-Based Deep Learning Book
#7"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.
Indeed, “Deep Learning Based Physics” seems a little more correct.
Re: Physics-Based Deep Learning Book
#8Why python? Doesn't Julia compose traditional ODE solvers with differential programming better?
is this the "why not rewrite it in rust" of the DL space?
Re: Physics-Based Deep Learning Book
#9Why python? Doesn't Julia compose traditional ODE solvers with differential programming better?
is this the "why not rewrite it in rust" of the DL space?
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.
Re: Physics-Based Deep Learning Book
#10What a coincidence! just before I come across this post,
I was reading this book this morning.