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Search results for julia automatic differentiation

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66 search results found

Zygote.jl ⭐ 1,415

21st century AD

Forwarddiff.jl ⭐ 833

Forward Mode Automatic Differentiation for Julia

Optimization.jl ⭐ 625

Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.

Tullio.jl ⭐ 559

Grassmann.jl ⭐ 429

⟨Grassmann-Clifford-Hodge⟩ multilinear differential geometric algebra

Chainrules.jl ⭐ 404

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

Enzyme.jl ⭐ 364

Julia bindings for the Enzyme automatic differentiator

Reversediff.jl ⭐ 327

Reverse Mode Automatic Differentiation for Julia

Surrogates.jl ⭐ 302

Surrogate modeling and optimization for scientific machine learning (SciML)

Taylorseries.jl ⭐ 299

Taylor polynomial expansions in one and several independent variables.

Chainrulescore.jl ⭐ 237

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.

Nilang.jl ⭐ 223

A differential eDSL that can run faster than light and go back to the past.

Neuraloperators.jl ⭐ 206

DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia

Integrals.jl ⭐ 191

A common interface for quadrature and numerical integration for the SciML scientific machine learning organization

Nnlib.jl ⭐ 187

Neural Network primitives with multiple backends

Tensors.jl ⭐ 164

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

Topopt.jl ⭐ 163

A package for binary and continuous, single and multi-material, truss and continuum, 2D and 3D topology optimization on unstructured meshes using automatic differentiation in Julia.

Autograd.jl ⭐ 160

Julia port of the Python autograd package.

Adcme.jl ⭐ 158

Automatic Differentiation Library for Computational and Mathematical Engineering

Omeinsum.jl ⭐ 152

One More Einsum for Julia! With runtime order-specification and high-level adjoints for AD

Yota.jl ⭐ 146

Reverse-mode automatic differentiation in Julia

Distributionsad.jl ⭐ 146

Automatic differentiation of Distributions using Tracker, Zygote, ForwardDiff and ReverseDiff

Nbodysimulator.jl ⭐ 122

A differentiable simulator for scientific machine learning (SciML) with N-body problems, including astrophysical and molecular dynamics

Implicitdifferentiation.jl ⭐ 107

Automatic differentiation of implicit functions

Inferopt.jl ⭐ 102

Combinatorial optimization layers for machine learning pipelines

Nonconvex.jl ⭐ 100

Toolbox for gradient-based and derivative-free non-convex constrained optimization with continuous and/or discrete variables.

Taylorintegration.jl ⭐ 99

ODE integration using Taylor's method, and more, in Julia

Preallocationtools.jl ⭐ 99

Tools for building non-allocating pre-cached functions in Julia, allowing for GC-free usage of automatic differentiation in complex codes

Tensornetworkad.jl ⭐ 77

Algorithms that combine tensor network methods with automatic differentiation

Dualnumbers.jl ⭐ 73

Julia package for representing dual numbers and for performing dual algebra

Multiscalearrays.jl ⭐ 69

A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations

Juliacomputation ⭐ 68

Repository for Common Ground C25

Nabla.jl ⭐ 65

A operator overloading, tape-based, reverse-mode AD

Symbolics.jl ⭐ 63

A symbolic math library written in Julia modelled off scmutils

Scientific Programming In Julia ⭐ 60

Repository for B0M36SPJ

Taylordiff.jl ⭐ 58

Taylor-mode automatic differentiation for higher-order derivatives

Autohedge.jl ⭐ 53

Automatic Options Hedging and Backtesting

Cmblensing.jl ⭐ 50

The automatically differentiable and GPU-compatible toolkit for CMB analysis.

Adseismic.jl ⭐ 47

A General Approach to Seismic Inversion Problems using Automatic Differentiation

Chainrulestestutils.jl ⭐ 45

Utilities for testing custom AD primitives.

Ad4sm.jl ⭐ 43

Automatic Differentiation for Solid Mechanics

differentiable boltzmann code

Protograd.jl ⭐ 31

Deep learning 99% fat free

Quiqbox.jl ⭐ 30

Exploring the computational power of fermionic quantum systems. Ab initio computation and basis set optimization for electronic structure problems.

Abstractoperators.jl ⭐ 28

Abstract operators for large scale optimization in Julia

Adfem.jl ⭐ 28

Innovative, efficient, and computational-graph-based finite element simulator for inverse modeling

Differentiablefactorizations.jl ⭐ 27

Differentiable matrix factorizations using ImplicitDifferentiation.jl.

Lazystack.jl ⭐ 26

Yaoblocks.jl ⭐ 25

Standard basic quantum circuit simulator building blocks.

Reversediffsparse.jl ⭐ 23

Reverse-mode automatic differentiation for sparse Hessians

Slicemap.jl ⭐ 22

Same-same but different

Tensorial.jl ⭐ 22

Statically sized tensors and related operations for Julia

Fwiflow.jl ⭐ 20

Elastic Full Waveform Inversion for Subsurface Flow Problems Using Intrusive Automatic Differentiation

Autodiffr ⭐ 18

Automatic Differentiation for R

Potentiallearning.jl ⭐ 17

An open source Julia library for active learning of interatomic potentials in atomistic simulations of materials. It incorporates elements of bayesian inference, machine learning, differentiable programming, software composability, and high-performance computing.

Implicitad.jl ⭐ 16

Automates adjoints. Forward and reverse mode algorithmic differentiation around implicit functions (not propagating AD through), as well as custom rules to allow for mixed-mode AD or calling external (non-AD compatible) functions within an AD chain.

Nngraph.jl ⭐ 15

Deep Learning library for the Julia language

Contmechtensors.jl ⭐ 13

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

Examodels.jl ⭐ 11

An algebraic modeling and automatic differentiation tool in Julia Language, specialized for SIMD abstraction of nonlinear programs.

Funmanifolds.jl ⭐ 11

Functional differential geometry in Julia

Control_neuralode ⭐ 9

On the use of NeuralODEs as optimal control policies: I.O. Sandoval, P. Petsagkourakis, E. A. del Rio-Chanona, “Neural ODEs as Feedback Policies for Nonlinear Optimal Control” The 22nd World Congress of the International Federation of Automatic Control (IFAC 2023)

Nonlinear regression in Julia

Manifolddiff.jl ⭐ 9

Differentiation on manifolds

Mestimation.jl ⭐ 6

Methods for M-estimation of statistical models

Altdistributions.jl ⭐ 6

Implementations for some distributions using a consistent API and AD-friendly code.

Kfestimate.jl ⭐ 5

Julia package for KF and EKF parameter estimation using Automatic Differentiation

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