autodiff
Experimental forward-mode AD for tensor networks.
Julia implementation of HyperDualNumbers
Differentiate python calls from Julia
desambiguation, ForwardDiff over Measurements
ChainRules.jl integration for Unitful.jl
A package which provides an API for querying differentiation results at multiple orders simultaneously
forward and reverse mode automatic differentiation primitives for Julia Base + StdLibs
ChainRulesCore compatible pullbacks using ForwardDiff
Fast jacobian computation through sparsity exploitation and matrix coloring
Total Variation Regularized Numerical Differentiation
The current implementation of `LinearAlgebra.eigen` does not support sensitivities. DifferentiableEigen.jl offers an `eigen` function that is differentiable by every AD-framework with support for C…
A common suite of test functions for stressing the robustness of differentiation tools.
Checkpointing for Automatic Differentiation
A Julia library of summation-by-parts (SBP) operators used in finite difference, Fourier pseudospectral, continuous Galerkin, and discontinuous Galerkin methods to get provably stable semidiscretiz…
Differentiating optimization programs w.r.t. program parameters
Differentiable wrapper for FrankWolfe.jl convex optimization routines
Reverse Mode Automatic Differentiation for Julia
Combinatorial optimization layers for machine learning pipelines
Zygote-differentiable way of flattening and unflattening data structures
Forward Mode Automatic Differentiation for Julia
Custom Bindings for Enzyme Automatic Differentiation Tool and Interfacing with JAX.
A simple shared suite of common derivative definitions
Rigorous function approximation using Taylor models in Julia
Taylor-mode automatic differentiation for higher-order derivatives