Google Season of Docs: Scientific Machine Learning (SciML) and Differential Equations

The SciML project is a participant organization for Google Season of Docs. In this program, technical writers are paid to work on various SciML open source documentation. Each of the writers are paired with a team of mentors to help them learn various aspects of computational science, from numerical differential equations and scientific machine learning, to parallel and symbolic-numeric computing.

Below are the proposed projects in this area. Technical writers may wish to do a combination of these projects. The mentors for the SciML projects are Chris Rackauckas, and Sam Isaacson.

Here are some possible projects:

Tutorial Writing


The SciML organization hosts the SciMLTutorials.jl repository which auto-builds websites and pdfs from tutorials. Tutorials generally center on features of DifferentialEquations.jl or on application domains. However, there are so many domains that could be covered in more depth, like:

And much much more.

Project Scope

This is simple! Technical writers who have expertise in areas like biological modeling may wish to contribute tutorials that showcase how to use the SciML tools to solve problems arising in their discipline. Thus this can be an exciting project to for technical folks interested in learning more about the SciML tools as the project devs have agreed to lend a hand in helping put together the demo code.

Measuring Success

Because of the large active userbase of the SciML software, accrewing tens of thousands of downloads each month and having a highly active presence on the Julialang Discourse and the Julia tag on StackOverflow, success can be measured by the number of users who are satisfied after being linked these tutorialson these Q&A forums. Success would be measured by having non-SciML devs posting links to these tutorials on these docs.


This project has a variable timeline. We suggest choosing 3 tutorials for a 6 month project, as on average we find a complete tutorial may take approximately two months to solidify. For a given tutorial, this gives about 3-4 weeks working on writing the code, first coming up with a draft of the code of the tutorial in about 2 weeks, and spending the rest of the time simplifying the code to make it more impactful. Then, 2 weeks are usually spent on writing the full story around the code: the descriptions of each section, introduction and conclusion. This gives about two weeks dedicated to creating more effective visualizations in the tutorial, which we find to be an average.

Potential Impact

Many university classes use the SciML ecosystem for its teaching, and thus classrooms all over the world will be improved. Tutorials that capture more domains will allow professors teaching biological modeling courses to not have to manually rewrite physics-based tutorials to match their curriculum. These tutorials will likely make it into the homework of many students!