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Search results for python automatic differentiation

automatic-differentiation x

80 search results found

Tangent ⭐ 2,066

Source-to-Source Debuggable Derivatives in Pure Python

Pennylane ⭐ 2,022

PennyLane is a cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network.

Pinocchio ⭐ 1,331

A fast and flexible implementation of Rigid Body Dynamics algorithms and their analytical derivatives

Aesara ⭐ 1,122

Aesara is a Python library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays.

Pymanopt ⭐ 668

Python toolbox for optimization on Riemannian manifolds with support for automatic differentiation

Aerosandbox ⭐ 573

Aircraft design optimization made fast through modern automatic differentiation. Composable analysis tools for aerodynamics, propulsion, structures, trajectory design, and much more.

Torchopt ⭐ 460

TorchOpt is an efficient library for differentiable optimization built upon PyTorch.

Deep Learning From Scratch 3 ⭐ 426

『ゼロから作る Deep Learning ❸』(O'Reilly Japan, 2020)

Optimal Transport tools implemented with the JAX framework, to get auto-diff, parallel and jit-able computations.

Betty: an automatic differentiation library for generalized meta-learning and multilevel optimization

Theano_lstm ⭐ 309

🔬 Nano size Theano LSTM module

Tensorlang ⭐ 303

Tensorlang, a differentiable programming language based on TensorFlow

Kayak is a library for automatic differentiation with applications to deep neural networks.

Tensorcircuit ⭐ 230

Tensor network based quantum software framework for the NISQ era

Jaxfluids ⭐ 203

Differentiable Fluid Dynamics Package

Sinkhornautodiff ⭐ 178

Toolbox to integrate optimal transport loss functions using automatic differentiation and Sinkhorn's algorithm

Torchquad ⭐ 142

Numerical integration in arbitrary dimensions on the GPU using PyTorch / TF / JAX

Jax_cosmo ⭐ 138

A differentiable cosmology library in JAX

Tensorflow Forward Ad ⭐ 130

Forward-mode Automatic Differentiation for TensorFlow

Neograd ⭐ 117

A deep learning framework created from scratch with Python and NumPy

Autoptim ⭐ 91

Automatic differentiation + optimization

Tensorgrad ⭐ 90

Differentiable Programming Tensor Networks

Autograd From Scratch ⭐ 82

Documented and Unit Tested educational Deep Learning framework with Autograd from scratch.

adam implements a collection of algorithms for calculating rigid-body dynamics in Jax, CasADi, PyTorch, and Numpy.

Catalyst ⭐ 75

A JIT compiler for hybrid quantum programs in PennyLane

Auto-differentiable and hardware-accelerated force density method

Quantumflow ⭐ 69

QuantumFlow: A Quantum Algorithms Development Toolkit

Differentiable Quantum Chemistry (only Differentiable Density Functional Theory and Hartree Fock at the moment)

Causing: CAUsal INterpretation using Graphs

Tensor decomposition with arbitrary expressions: inner, outer, elementwise operators; nonlinear transformations; and more.

Bayesian Optimization in JAX

Hookean Springs Pytorch ⭐ 43

Hookean springs in PyTorch

Tinyflow ⭐ 42

A simple deep learning framework that supports automatic differentiation and GPU acceleration.

Qualia2.0 ⭐ 41

Qualia is a deep learning framework deeply integrated with automatic differentiation and dynamic graphing with CUDA acceleration. Qualia was built from scratch.

Num Dual ⭐ 39

Generalized (hyper-) dual numbers in rust

Keras-like APIs for JAX framework

Python package for Geometric / Clifford Algebra with TensorFlow

Deep Learning Optimization Algorithms ⭐ 37

Visualization of various deep learning optimization algorithms using PyTorch automatic differentiation and optimizers.

Differentiable molecular simulation of proteins with a coarse-grained potential

Pennylane Rigetti ⭐ 35

This PennyLane plugin allows the Rigetti Forest QPUs, QVM, and wavefunction simulator to optimize quantum circuits.

A scalable physics engine and multibody dynamics library implemented with JAX. With JIT batteries 🔋

Numpy Nn Model ⭐ 32

Сustom torch style machine learning framework with automatic differentiation implemented on numpy, allows build GANs, VAEs, etc.

Compas_cem ⭐ 31

Inverse design of 3D trusses with automatic differentiation

Pyerrors ⭐ 29

Error propagation and statistical analysis for Markov chain Monte Carlo simulations in lattice QCD and statistical mechanics using autograd

Jax Fenics ⭐ 27

Differentiable interface to FEniCS for JAX

Springs Integration Pytorch ⭐ 25

Numerical integration methods for mass-springs systems using PyTorch's autodiff

A Fisher information matrix python package for GW detector networks.

Randomizedautomaticdifferentiation ⭐ 19

Experiment code for "Randomized Automatic Differentiation"

Extrinsic_calibration ⭐ 16

Motion Based Multi-Sensor Extrinsic Calibration

Automatic Functional Differentiation in JAX

Dual Numbers And Automatic Differentiation Using Python ⭐ 16

Implemented the forward mode of automatic differentiation with the help of dual numbers using Python.

Easy interoperability with Automatic Differentiation libraries through NumPy interface to Firedrake and FEniCS

A Python library for training neural ODEs.

Toeffipy ⭐ 13

ToeffiPy is a PyTorch like autograd/deep learning library based only on NumPy.

Cardoon Electronic Circuit Simulator

A simple library to run variational inference on Stan models.

GRAPE with autograd on the TDSE and LME in Python

A small framework that can perform automatic differentiation to calculate first-order gradients of numpy arrays.

Material Definition with Automatic Differentiation

Gradflow ⭐ 10

A small, educational autograd system with deep neural networks support

SemiFlow is a deep learning framework with automatic differentiation and automatic shape inference, developing from Numpy. 一个基于Numpy支持自动求导的深度学习框架

Automatic differentiation in python

Dali Cython ⭐ 9

🍭 Dali in Python

Profess Ad ⭐ 8

PyTorch-based auto-differentiable orbital-free density functional theory package

Automatic-Differentiation-Enabled Plasma Transport

Automatic Differentiation for Python

Physics Driven Ml ⭐ 8

Physics-driven machine learning using PyTorch and Firedrake

A small framework that can perform automatic differentiation.

Pennylane Qulacs ⭐ 7

Contains the PennyLane Qulacs plugin

Ot Gradients ⭐ 7

Tad Dftd3 ⭐ 6

PyTorch Autodiff DFT-D3 Implementation.

An imperative deep learning framework with customized GPU and CPU backend

Tad Dftd4 ⭐ 6

PyTorch Autodiff DFT-D4 Implementation.

Openredukti ⭐ 6

OpenRedukti is a C++ library for Interest Rate Swaps and Fras, supports bootstrapping of Interest Rate Curves, computing NPV and sensitivities using automatic/algorithmic differentiation. It provides a scripting environment in Python and Ravi (a Lua dialect).

Numerical Continuation using just the residual. 5k+ downloads

Automatic differentiation deep neural network framework.

💤 Just Another Neural network

Automatic Differentiation ⭐ 5

A publicly available Python package for automatic differentiation (i.e. similar to TensorFlow, PyTorch) with built-in support for root finding, optimization, quadratic splines, and animated visualization.

Tensortrax ⭐ 5

Math on (Hyper-Dual) Tensors with Trailing Axes

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