All Projects → JuliaDiff → ChainRulesCore.jl

JuliaDiff / ChainRulesCore.jl

Licence: other
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.

Programming Languages

julia
2034 projects

Projects that are alternatives of or similar to ChainRulesCore.jl

Scientific-Programming-in-Julia
Repository for B0M36SPJ
Stars: ✭ 32 (-79.08%)
Mutual labels:  automatic-differentiation
TensorAlgDiff
Automatic Differentiation for Tensor Algebras
Stars: ✭ 26 (-83.01%)
Mutual labels:  automatic-differentiation
Quadrature.jl
A common interface for quadrature and numerical integration for the SciML scientific machine learning organization
Stars: ✭ 83 (-45.75%)
Mutual labels:  automatic-differentiation
autodiffr
Automatic Differentiation for R
Stars: ✭ 21 (-86.27%)
Mutual labels:  automatic-differentiation
dopt
A numerical optimisation and deep learning framework for D.
Stars: ✭ 28 (-81.7%)
Mutual labels:  automatic-differentiation
Birch
A probabilistic programming language that combines automatic differentiation, automatic marginalization, and automatic conditioning within Monte Carlo methods.
Stars: ✭ 80 (-47.71%)
Mutual labels:  automatic-differentiation
MissionImpossible
A concise C++17 implementation of automatic differentiation (operator overloading)
Stars: ✭ 18 (-88.24%)
Mutual labels:  automatic-differentiation
Yota.jl
Reverse-mode automatic differentiation in Julia
Stars: ✭ 113 (-26.14%)
Mutual labels:  automatic-differentiation
ad-lens
Automatic Differentiation using Pseudo Lenses. Neat.
Stars: ✭ 16 (-89.54%)
Mutual labels:  automatic-differentiation
tensorgrad
Differentiable Programming Tensor Networks
Stars: ✭ 102 (-33.33%)
Mutual labels:  automatic-differentiation
YaoBlocks.jl
Standard basic quantum circuit simulator building blocks. (archived, for it is moved to Yao.jl)
Stars: ✭ 26 (-83.01%)
Mutual labels:  automatic-differentiation
FwiFlow.jl
Elastic Full Waveform Inversion for Subsurface Flow Problems Using Intrusive Automatic Differentiation
Stars: ✭ 24 (-84.31%)
Mutual labels:  automatic-differentiation
HamiltonianSolver
Numerically solves equations of motion for a given Hamiltonian function
Stars: ✭ 51 (-66.67%)
Mutual labels:  automatic-differentiation
Tensorial.jl
Statically sized tensors and related operations for Julia
Stars: ✭ 18 (-88.24%)
Mutual labels:  automatic-differentiation
diffhask
DSL for forward and reverse mode automatic differentiation in Haskell. Port of DiffSharp.
Stars: ✭ 26 (-83.01%)
Mutual labels:  automatic-differentiation
xcfun
XCFun: A library of exchange-correlation functionals with arbitrary-order derivatives
Stars: ✭ 50 (-67.32%)
Mutual labels:  automatic-differentiation
autodiff
A .NET library that provides fast, accurate and automatic differentiation (computes derivative / gradient) of mathematical functions.
Stars: ✭ 69 (-54.9%)
Mutual labels:  automatic-differentiation
dace
Differential Algebra Computational Toolbox
Stars: ✭ 16 (-89.54%)
Mutual labels:  automatic-differentiation
Nabla.jl
A operator overloading, tape-based, reverse-mode AD
Stars: ✭ 54 (-64.71%)
Mutual labels:  automatic-differentiation
bayex
Bayesian Optimization in JAX
Stars: ✭ 24 (-84.31%)
Mutual labels:  automatic-differentiation

ChainRulesCore

Build Status Coverage Code Style: Blue ColPrac: Contributor's Guide on Collaborative Practices for Community Packages DOI

Docs:

The ChainRulesCore package provides a light-weight dependency for defining sensitivities for functions in your packages, without you needing to depend on ChainRules itself.

This will allow your package to be used with ChainRules.jl, which aims to provide a variety of common utilities that can be used by downstream automatic differentiation (AD) tools to define and execute forward-, reverse-, and mixed-mode primitives.

This package is a work in progress; PRs welcome!

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].