#automatic-differentiation (30 Repositories)
Ranked open-source repositories tagged with #automatic-differentiation, scored by pull request acceptance likelihood and maintainer engagement velocity.
78.3%
41.4h
30 repositories tagged #automatic-differentiation
kmolan/multicalc-rust
Scientific computing that fits on a microcontroller. Estimation, control, kinematics, Lie groups, calculus, autodiff and linear algebra in stable no_std Rust with no heap, no panics and no unsafe. Run the same code on your laptop and your Cortex-M0.
EnzymeAD/Enzyme
High-performance automatic differentiation of LLVM and MLIR.
PennyLaneAI/catalyst
A JIT compiler for hybrid quantum programs in PennyLane
EnzymeAD/Enzyme.jl
Julia bindings for the Enzyme automatic differentiator
pasteurlabs/tesseract-core
Universal components for differentiable scientific computing. :package:
SciML/Optimization.jl
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.
PennyLaneAI/pennylane
PennyLane is an open-source quantum software platform for quantum computing, quantum machine learning, and quantum chemistry. Create meaningful quantum algorithms, from inspiration to implementation.
stan-dev/math
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.
JuliaDiff/DifferentiationInterface.jl
An interface to various automatic differentiation backends in Julia.
JuliaTopOpt/TopOpt.jl
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.
MITgcm/MITgcm
M.I.T General Circulation Model master code and documentation repository
born-ml/born
Born is a modern ML framework for Go — train and deploy models as single binaries. Pure Go, zero CGO, GPU accelerated.
SciML/Integrals.jl
A common interface for quadrature and numerical integration for the SciML scientific machine learning organization
JuliaDecisionFocusedLearning/InferOpt.jl
Combinatorial optimization layers for machine learning pipelines
HIPS/autograd
Efficiently computes derivatives of NumPy code.
SciML/Surrogates.jl
Surrogate modeling and optimization for scientific machine learning (SciML)
SciML/NBodySimulator.jl
A differentiable simulator for scientific machine learning (SciML) with N-body problems, including astrophysical and molecular dynamics
SciML/PreallocationTools.jl
Tools for building non-allocating pre-cached functions in Julia, allowing for GC-free usage of automatic differentiation in complex codes
grimme-lab/dxtb
Efficient And Fully Differentiable Extended Tight-Binding
JuliaDiff/ReverseDiff.jl
Reverse Mode Automatic Differentiation for Julia
palle-k/DL4S
Accelerated tensor operations and dynamic neural networks based on reverse mode automatic differentiation for every device that can run Swift - from watchOS to Linux
JuliaDiff/ForwardDiff.jl
Forward Mode Automatic Differentiation for Julia
PerezHz/TaylorIntegration.jl
ODE integration using Taylor's method, and more, in Julia
auto-differentiation/xad
Fast, easy automatic differentiation in C++
JuliaDiff/TaylorSeries.jl
Taylor polynomial expansions in one and several independent variables.
FluxML/Zygote.jl
21st century AD
JuliaDiff/ChainRulesCore.jl
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.
Ferrite-FEM/Tensors.jl
Efficient computations with symmetric and non-symmetric tensors with support for automatic differentiation.
under-Peter/OMEinsum.jl
One More Einsum for Julia! With runtime order-specification and high-level adjoints for AD