SciML: Differentiable Modeling and Simulation Combined with Machine Learning

The SciML organization is a collection of tools for solving equations and modeling systems developed in the Julia programming language with bindings to other languages such as R and Python. The organization provides well-maintained tools which compose together as a coherent ecosystem. It has a coherent development principle, unified APIs over large collections of equation solvers, pervasive differentiability and sensitivity analysis, and features many of the highest performance and parallel implementations one can find.

Scientific Machine Learning (SciML) = Scientific Computing + Machine Learning

Where to Start?

• Want to get started running some code? Check out the Getting Started tutorials.
• What is SciML? Check out our Overview.
• Want to see some cool end-to-end examples? Check out the SciML Showcase.
• Curious about our performance claims? Check out the SciML Open Benchmarks.
• Want to learn more about how SciML does scientific machine learning? Check out the SciML Book (from MIT's 18.337 graduate course).
• Want to chat with someone? Check out our chat room and forums.
• Want to see our code? Check out the SciML GitHub organization.

And for diving into the details, use the bar on the top to navigate to the submodule of interest!