75 Open source projects that are alternatives of or similar to Dcpp

Bayesian Optimization in JAX

Abstract operators for large scale optimization in Julia

Gorgonia is a library that helps facilitate machine learning in Go.

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

Differentiable molecular simulation of proteins with a coarse-grained potential

Forward Mode Automatic Differentiation for Julia

Automatic Differentiation for Tensor Algebras

Intimate Affection Auditor

Repository for B0M36SPJ

Tensorlang, a differentiable programming language based on TensorFlow

Differential Algebra Computational Toolbox

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

The Control Toolbox - An Open-Source C++ Library for Robotics, Optimal and Model Predictive Control

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

Differentiable interface to FEniCS for JAX using dolfin-adjoint/pyadjoint

A probabilistic programming language that combines automatic differentiation, automatic marginalization, and automatic conditioning within Monte Carlo methods.

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

A numerical optimisation and deep learning framework for D.

Algorithms that combine tensor network methods with automatic differentiation

Automatic Differentiation for R

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

A concise C++17 implementation of automatic differentiation (operator overloading)

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

Causing: CAUsal INterpretation using Graphs

Tensors and differentiable operations (like TensorFlow) in Rust

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.

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

automatic differentiation made easier for C++

Reverse-mode automatic differentiation in Julia

C++ template library for probabilistic programming

DSL for forward and reverse mode automatic differentiation in Haskell. Port of DiffSharp.

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.

Differentiable Programming Tensor Networks

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

Numerically solves equations of motion for a given Hamiltonian function

A Programming Language for Deep Learning

A .NET library that provides fast, accurate and automatic differentiation (computes derivative / gradient) of mathematical functions.

Self-contained Machine Learning and Natural Language Processing library in Go

Automatic Differentiation using Pseudo Lenses. Neat.

High-performance automatic differentiation of LLVM.

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

Julia bindings for the Enzyme automatic differentiator

Standard basic quantum circuit simulator building blocks. (archived, for it is moved to Yao.jl)

Shape-Safe Symbolic Differentiation with Algebraic Data Types

Statically sized tensors and related operations for Julia

Owl - OCaml Scientific and Engineering Computing @ http://ocaml.xyz

XCFun: A library of exchange-correlation functionals with arbitrary-order derivatives

🔬 Nano size Theano LSTM module

Fortran compilers, preprocessors, static analyzers, transpilers, IDEs, build systems, etc.

QuantumFlow: A Quantum Algorithms Development Toolkit

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

⟨Leibniz-Grassmann-Clifford⟩ differential geometric algebra / multivector simplicial complex

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

A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends

A symbolic math library written in Julia modelled off scmutils

Automatic Differentiation Library for Computational and Mathematical Engineering

Source Code Generation for Automatic Differentiation using Operator Overloading

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

A simple library for creating complex neural networks

Pytorch implementation of "DeepFlow: History Matching in the Space of Deep Generative Models"