Search

The Stan Math Library is a C++ template library for automatic differentiation of any order using forward, reverse, and mixed modes. It includes a range of built-in functions for probabilistic modeling, linear algebra, and equation solving.

Source Code Generation for Automatic Differentiation using Operator Overloading

CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT etc. It can be used from C++, Python, Matlab/Octave, Julia or Javascript

Forward Mode Automatic Differentiation for Julia

clad -- automatic differentiation for C/C++

Python toolbox for optimization on Riemannian manifolds with support for automatic differentiation

Efficient computations with symmetric and non-symmetric tensors with support for automatic differentiation.

forward and reverse mode automatic differentiation primitives for Julia Base + StdLibs

High-performance automatic differentiation of LLVM and MLIR.

Omega-AI is a Java-based deep learning framework that helps you quickly build neural networks for inference and training. Its engine supports automatic differentiation, multithreading, and GPU acceleration with CUDA and cuDNN.

A Package for Automatic Differentiation of Algorithms Written in C/C++

The fastest automatic differentiation library for C++, with Python bindings. Forward, adjoint and higher-order derivatives, checkpointing, Eigen support and JIT. Reproducible benchmarks against CppAD, Adept and autodiff.

Custom Bindings for Enzyme Automatic Differentiation Tool and Interfacing with JAX.

An Automatic Differentiation-based Waveform Inversion Framework Implemented in PyTorch.