SciML Scientific Machine Learning Developer Documentation

This is the developer documentation and Contributor's Guide for the SciML ecosystem. It explains the common interface and some the package internals to help developers contribute.

If you have any questions, or just want to chat about solvers/using the package, please feel free to use the Gitter channel. For bug reports, feature requests, etc., please submit an issue.


The SciML ecosystem is built around the common interface. The common interface is a type-based interface where users define problems as a type, and solvers plug into the ecosystem by defining an algorithm to give a new dispatch to


There is a top level solve and init function which is in DiffEqBase.jl that handles distribution and function input (along with extra warnings) before sending the problems to the packages.

There is then an ecosystem of add-on components which use the common solver interface to add analysis tools for differential equations.

Contributing to the Ecosystem

There are many ways to help the ecosystem. One way you can contribute is to give pull requests (PRs) to existing packages. Another way to contribute is to add your own package to the ecosystem. Adding your own package to the ecosystem allows you to keep executive control and licensing over your methods, but allows users of DifferentialEquations.jl to use your methods via the common interface, and makes your package compatible with the add-on tools (sensitivity analysis, parameter estimation, etc). Note that, in order for the method to be used as a default, one is required to move their package to the SciML organization so that way common maintenance (such as fixing deprication warnings, updating tests to newer versions, and emergency fixes / disabling) can be allowed by SciML members. However, the lead developer of the package maintains administrative control, and thus any change to the core algorithms by other SciML members will only be given through PRs.

Even if you don't have the time to contribute new solver algorithms or add-on tools, there's always ways to help! Improved plot recipes and new series recipes are always nice to add more default plots. It is always helpful to have benchmarks between different algorithms to see "which is best". Adding examples IJulia notebooks to DiffEqTutorials.jl is a good way to share knowledge about DifferentialEquations.jl. Also, please feel free to comb through the solvers and look for ways to make them more efficient. Lastly, the documentation could always use improvements. If you have any questions on how to help, just ask them in the Gitter!

Code of Conduct

All contributors must adhere to the NumFOCUS Code of Conduct. Treat everyone with respect. Failure to comply will result in individuals being banned from the community.

Contributor Guide

Algorithm Development Tools

The following algorithm development tools are provided by DiffEqDevTools.jl

Internal Documentation


The documentation of this SciML package was built using these direct dependencies,
Status `~/work/DiffEqDevDocs.jl/DiffEqDevDocs.jl/docs/Project.toml`
  [fa972c1a] DiffEqDevDocs v1.0.0 `~/work/DiffEqDevDocs.jl/DiffEqDevDocs.jl`
  [f3b72e0c] DiffEqDevTools v2.32.1
  [e30172f5] Documenter v0.27.23
  [1dea7af3] OrdinaryDiffEq v6.33.1
and using this machine and Julia version.
Julia Version 1.8.3
Commit 0434deb161e (2022-11-14 20:14 UTC)
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 2 × Intel(R) Xeon(R) Platinum 8272CL CPU @ 2.60GHz
  LIBM: libopenlibm
  LLVM: libLLVM-13.0.1 (ORCJIT, skylake-avx512)
  Threads: 1 on 2 virtual cores
A more complete overview of all dependencies and their versions is also provided.
Status `~/work/DiffEqDevDocs.jl/DiffEqDevDocs.jl/docs/Manifest.toml`
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⌅ [ba0b0d4f] Krylov v0.8.4
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  [1a1011a3] SharedArrays
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Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m`
You can also download the manifest file and the project file.