SciML Scientific Machine Learning Documentation and Tutorials

The SciML organization is an opinionated collection of tools for scientific machine learning and differential equation modeling. The organization provides well-maintained tools which compose together as a coherent ecosystem. The following are the relevant resources for users interested in the functionality.

SciML-Wide Documentation

Differential Equations

These resources cover:

Partial Differential Equation Modeling and Solving

Core Mathematical Systems


Estimation, Inference, and Model Discovery

Honorable mention to Turing.jl, a probabilistic programming language that composes with the SciML tools.

Surrogate Acceleration

Modeling Languages and Domain-Specific Languages

Modeling Tools and Primatives

Numerical Tools and Primatives

Developer Documentation

Please see the developer documentation for information on getting started with developing in the SciML organization. Please see Colprac for the community development practices.

External Tutorials and Teaching Materials