ADEL is an open-source C++ template library for Automatic Differentiation in forward mode. Works with CUDA out of the box.
ADEL is an easy to use forward mode automated differentiation library using C++ templates.
In this version:
- Automatic differentiation (forward mode) using C++ templates
- CUDA support
- Gradient and Hessian for partial derivatives
- Newton-Raphson method
The library is distributed under MIT license. Your feedback and contribution are highly appreciated.
Licensing: open source
Entries in our publication database that actually use ADEL in the numerical experiments: 0
The following diagram shows these entries versus the year of the publication.