Julia bindings for the Enzyme automatic differentiator
Efficiently computes derivatives of NumPy code.
A fast and flexible implementation of Rigid Body Dynamics algorithms and their analytical derivatives
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.
High-performance automatic differentiation of LLVM and MLIR.
21st century AD
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
Aircraft design optimization made fast through computational graph transformations (e.g., automatic differentiation). Composable analysis tools for aerodynamics, propulsion, structures, trajectory design, and much more.
Owl - OCaml Scientific Computing @ https://ocaml.xyz
Forward Mode Automatic Differentiation for Julia
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.
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.
Introductions to key concepts in quantum programming, as well as tutorials and implementations from cutting-edge quantum computing research.
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TorchOpt is an efficient library for differentiable optimization built upon PyTorch.
⟨Grassmann-Clifford-Hodge⟩ differential geometric algebra
Differentiable ensemble solvers for differential equations on GPUs, in JAX