Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
SciMLTutorials.jl holds PDFs, webpages, and interactive Jupyter notebooks showing how to utilize the software in the SciML Scientific Machine Learning ecosystem. This set of tutorials was made to complement the documentation and the devdocs by providing practical examples of the concepts. For more details, please consult the docs.
To run the tutorials interactively via Jupyter notebooks, install the package and open the tutorials like:
using Pkg pkg"add https://github.com/SciML/SciMLTutorials.jl" using SciMLTutorials SciMLTutorials.open_notebooks()
First of all, make sure that your current directory is
SciMLTutorials. All of the files are generated from the Weave.jl files in the
tutorialsfolder. To run the generation process, do for example:
using Pkg, SciMLTutorials cd(joinpath(dirname(pathof(SciMLTutorials)), "..")) Pkg.pkg"activate ." Pkg.pkg"instantiate" SciMLTutorials.weave_file("introduction","01-ode_introduction.jmd")
To generate all of the notebooks, do:
If you add new tutorials which require new packages, simply updating your local environment will change the project and manifest files. When this occurs, the updated environment files should be included in the PR.