GitHub

Curated Learning, Teaching, and Training Resources

While the SciML documentation is made to be comprehensive, there will always be good alternative resources. The purpose of this section of the documentation is to highlight the alternative resources which can be helpful for learning how to use the SciML Open-Source Software libraries.

JuliaCon and SciMLCon Videos

Many tutorials and introductions to packages have been taught through previous JuliaCon/SciMLCon workshops and talks. The following is a curated list of such training videos:

SciML Book: Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications

The book Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications is a compilation of the lecture notes from the MIT Course 18.337J/6.338J: Parallel Computing and Scientific Machine Learning. It contains a walkthrough of many of the methods implemented in the SciML libraries, as well as how to understand much of the functionality at a deeper level. This course was intended for MIT graduate students in engineering, computer science, and mathematics and thus may have a high prerequisite requirement than many other resources.

sir-julia: Various implementations of the classical SIR model in Julia

For those who like to learn by example, the repository sir-julia is a great resource! It showcases how to use the SciML libraries in many different ways to simulate different variations of the classic SIR epidemic model.

Other Books Featuring SciML