Forward Mode Automatic Differentiation for Julia

AD-backend agnostic system defining custom forward and reverse mode rules. This is the light weight core to allow you to define rules for your functions in your packages, without depending on any particular AD system.

A common interface for quadrature and numerical integration for the SciML scientific machine learning organization

Julia package for representing dual numbers and for performing dual algebra

Reverse automated differentiation from source

Optimal addition-chain exponentiation for Julia

Reachability analysis for closed-loop control systems in Julia

Julia implementation of HyperDualNumbers