SciMLTutorials.jl
SciMLBook
| SciMLTutorials.jl | SciMLBook | |
|---|---|---|
| 1 | 4 | |
| 709 | 1,789 | |
| 0.6% | 0.8% | |
| 1.5 | 4.9 | |
| 5 days ago | 20 days ago | |
| CSS | HTML | |
| GNU General Public License v3.0 or later | - |
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SciMLTutorials.jl
SciMLBook
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SciML Textbook
I've been working on and off using SciML. I just found out they have an e-book: https://book.sciml.ai/
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What's Great about Julia?
I'm hoping the new SciML docs can become a good enough source for beginners looking to do scientific computing (https://docs.sciml.ai/Overview/stable/). It's not there yet, we literally started redirecting links to the new docs on Monday so that's how new it is, it's already moving in the direction of having a lot of materials for new users (in scientific computing specifically, this is not and will not be a general Julia resource) before ever hitting deeper features.
Though if someone wants to dive deep into the language, I'd plug my own SciML course notes: https://book.sciml.ai/, which again is not for general usage but scientific computing but does show a lot about good programming styles (see https://book.sciml.ai/notes/02-Optimizing_Serial_Code/).
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SciML/SciMLBook: Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)
This was previously the https://github.com/mitmath/18337 course website, but now in a new iteration of the course it is being reset. To avoid issues like this in the future, we have moved the "book" out to its own repository, https://github.com/SciML/SciMLBook, where it can continue to grow and be hosted separately from the structure of a course. This means it can be something other courses can depend on as well. I am looking for web developers who can help build a nicer webpage for this book, and also for the SciMLBenchmarks.
What are some alternatives?
SciMLBenchmarks.jl - Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
cs229-2019-summer - All notes and materials for the CS229: Machine Learning course by Stanford University
DiffEqSensitivity.jl - A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, and more for ODEs, SDEs, DDEs, DAEs, etc. [Moved to: https://github.com/SciML/SciMLSensitivity.jl]
18337 - 18.337 - Parallel Computing and Scientific Machine Learning
DiffEqOperators.jl - Linear operators for discretizations of differential equations and scientific machine learning (SciML)
Accessors.jl - Update immutable data
auto-07p - AUTO is a publicly available software for continuation and bifurcation problems in ordinary differential equations originally written in 1980 and widely used in the dynamical systems community.
18S096SciML - 18.S096 - Applications of Scientific Machine Learning
Setfield.jl - Update deeply nested immutable structs.
OrdinaryDiffEq.jl - High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)