https://julialang.org/

Julia DocumentationJulia was designed from the beginning for high performance. Julia programs compile to efficient native code for multiple platforms via LLVM.

Julia is provided under the MIT license, free for everyone to use. All source code is publicly viewable on GitHub.

https://docs.julialang.org/

Learning Julia

https://julialang.org/learning/

Julia for Numerical Computation in MIT Courses

https://github.com/mitmath/julia-mit

Using Julia's Type System For Hidden Performance GainsHigh-level dynamic programming languages (as opposed to low-level languages like C or static languages like Java) are essential for interactive exploration of computational science. They allow you to play with matrices, computations on large datasets, plots, and so on without having to worry about managing memory, declaring types, or other minutiae—you can open up a window and start typing commands to immediately get results.

The traditional problem with high-level dynamic languages, however, is that they are slow: as soon as you run into a problem that cannot easily be expressed in terms of built-in library functions operating on large blocks of data ("vectorized" code), you find that your code is suddenly orders of magnitude slower that equivalent code in a low-level language. The typical solution has been to switch to another language (e.g. C or Fortran) to write key computational kernels, calling these from the high-level language (e.g. Matlab or Python) as needed, but this is vastly more difficult than writing code purely in a high-level language and imposes a steep barrier on anyone hoping to transition from casual experimentation to "serious" numerical computation. Julia mostly eliminates this issue, because it is carefully designed to exploit a "just-in-time compiler" called LLVM, making it possible to write high-level code in Julia that achieves near-C speed.

https://www.stochasticlifestyle.com/usi ... snotation/

7 Julia Gotchas and How to Handle Them

https://www.stochasticlifestyle.com/7-j ... as-handle/

Type-Dispatch Design: Post Object-Oriented Programming for Julia

https://www.stochasticlifestyle.com/typ ... ing-julia/

A Comparison Between Differential Equation Solver Suites In MATLAB, R, Julia, Python, C, Mathematica, Maple, and Fortran

http://www.stochasticlifestyle.com/comp ... c-fortran/

I Like Julia Because It Scales and Is Productive: Some Insights From A Julia Developer

https://www.stochasticlifestyle.com/lik ... developer/

Algorithm efficiency comes from problem information

https://www.stochasticlifestyle.com/alg ... formation/

Why Numba and Cython are not substitutes for Julia

https://www.stochasticlifestyle.com/why ... for-julia/

Some State of the Art Packages in Julia v1.0

https://www.stochasticlifestyle.com/som ... ulia-v1-0/

Julia and DifferentialEquations.jl

https://www.youtube.com/watch?v=zJ3R6vOhibA

A Deep Introduction to Julia for Data Science and Scientific Computing

http://ucidatascienceinitiative.github.io/IntroToJulia/

The Essential Tools of Scientific Machine Learning (Scientific ML)

http://www.stochasticlifestyle.com/the- ... ntific-ml/

How To Train Interpretable Neural Networks That Accurately Extrapolate From Small Data

https://www.stochasticlifestyle.com/how ... mall-data/

Universal Differential Equations for Scientific Machine Learning

https://www.youtube.com/watch?v=bBH8HVEr0-A

Parallel Computing and Scientific Machine Learning (MIT Course)

https://github.com/mitmath/18337

Special Subject in Mathematics: Applications of Scientific Machine Learning (MIT Course)

https://github.com/mitmath/18S096SciML

SciML: An Open Source Software Organization for Scientific Machine Learning

https://sciml.ai/2020/03/29/SciML.html

Almost Trivial: Parallelizing a Specialized Matrix Type in Julia

https://mdavezac.github.io/blog/

Julia Programming’s Dramatic Rise in HPC and Elsewhere

https://www.hpcwire.com/2020/01/14/juli ... elsewhere/

Large Scale WinD Farm Optimization Integrating Julia with OpenMDAO

https://www.youtube.com/watch?v=_V7xrvXiESc

Link to a previous forum post on some of the Julia packages (and some other things)

https://forum.freecadweb.org/viewtopic.php?f=8&t=40775