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.
A JIT compiler for hybrid quantum programs in PennyLane
PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Train a quantum computer the same way as a neural network.
Introductions to key concepts in quantum programming, as well as tutorials and implementations from cutting-edge quantum computing research.
High-performance automatic differentiation of LLVM and MLIR.
Differentiable Wolski twiss matrices computation for arbitrary dimension stable symplectic matrices
A Differentiable THB-spline module implemented in JAX and PyTorch
Julia bindings for the Enzyme automatic differentiator
Several Haskell packages for numerical optimizations.
A fast and flexible implementation of Rigid Body Dynamics algorithms and their analytical derivatives
An interface to various automatic differentiation backends in Julia.
A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends
A differentiable physics engine and multibody dynamics library for control and robot learning.
Julia interface for ColPack
forward mode automatic differentiation using dual numbers
A generic library for linear and non-linear Gaussian smoothing problems. The code leverages JAX and implements several linearization algorithms, both in a sequential and parallel fashion, as well as efficient gradient rules for computing gradients of required quantities (such as the pseudo-loglikelihood of the system).
Taylor polynomial expansions in one and several independent variables.
Taylor-mode automatic differentiation for higher-order derivatives
M.I.T General Circulation Model master code and documentation repository
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