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SciMLTutorials.jl holds PDFs, webpages, and interactive Jupyter notebooksshowing how to utilize the software in theSciML Scientific Machine Learning ecosystem.This set of tutorials was made to complement thedocumentationand thedevdocsby providing practical examples of the concepts. For more details, pleaseconsult the docs.

To view the SciML Tutorials, go totutorials.sciml.ai. By default, thiswill lead to the latest tagged version of the tutorials. To see the in-development version of the tutorials, go tohttps://tutorials.sciml.ai/dev/.

Static outputs in pdf, markdown, and html reside inSciMLTutorialsOutput.

To generate the interactive notebooks, first install the SciMLTutorials, instantiate theenvironment, and then runSciMLTutorials.open_notebooks(). This looks as follows:

]add SciMLTutorials#master]activate SciMLTutorials]instantiateusing SciMLTutorialsSciMLTutorials.open_notebooks()

The tutorials will be generated at yourpwd() in a folder calledgenerated_notebooks.

Note that when running the tutorials, the packages are not automatically added. Thus youwill need to add the packages manually or use the internal Project/Manifest tomls toinstantiate the correct packages. This can be done by activating the folder of the tutorials.For example,

using PkgPkg.activate(joinpath(pkgdir(SciMLTutorials),"tutorials","models"))Pkg.instantiate()

will add all of the packages required to run any tutorial in themodels folder.

All of the files are generated from the Weave.jl files in thetutorials folder. The generation process runs automatically,and thus one does not necessarily need to test the Weave process locally. Instead, simply open a PR that adds/updates afile in the "tutorials" folder and the PR will generate the tutorial on demand. Its artifacts can then be inspected in theBuildkite as described below before merging. Note that it will use the Project.toml and Manifest.toml of the subfolder, soany changes to dependencies requires that those are updated.

Report any bugs or issues atthe SciMLTutorials repository.

To see tutorial results before merging, click into the BuildKite, click ontoArtifacts, and then investigate the trained results.

To run the generation process, do for example:

]activate SciMLTutorials# Get all of the packagesusing SciMLTutorialsSciMLTutorials.weave_file(joinpath(pkgdir(SciMLTutorials),"tutorials","models"),"01-classical_physics.jmd")

To generate all of the files in a folder, for example, run:

SciMLTutorials.weave_folder(joinpath(pkgdir(SciMLTutorials),"tutorials","models"))

To generate all of the notebooks, do:

SciMLTutorials.weave_all()

Each of the tuturials displays the computer characteristics at the bottom ofthe benchmark.