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.
SciMLTutorials.jl is an extended set of tutorials for the SciML open source software organization. It contains many complete workflow examples on large-scale problems that may be too large or complex for normal documenation, but good materials for users to learn from.
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:
- Intro to solving differential equations in Julia
- JuliaCon 2020 | Doing Scientific Machine Learning (SciML) With Julia
- Simulating Big Models in Julia with ModelingToolkit | Workshop | JuliaCon 2021
- Structural Identifiability Tools in Julia: A Tutorial | Ilia Ilmer | SciMLCon 2022
- JuliaCon 2018 | Solving Partial Differential Equations with Julia | Chris Rackauckas
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.
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.
- Nonlinear Dynamics: A Concise Introduction Interlaced with Code
- Numerical Methods for Scientific Computing: The Definitive Manual for Math Geeks
- Fundamentals of Numerical Computation
- Statistics with Julia
- Statistical Rethinking with Julia
- The Koopman Operator in Systems and Control
- "All simulations have been performed in Julia, with additional Julia packages: LinearAlgebra.jl, Random.jl, Plots.jl, Lasso.jl, DifferentialEquations.jl"