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.

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

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

DRIP Fixed Income is a collection of Java libraries for Instrument/Trading Conventions, Treasury Futures/Options, Funding/Forward/Overnight Curves, Multi-Curve Construction/Valuation, Collateral Valuation and XVA Metric Generation, Calibration and Hedge Attributions, Statistical Curve Construction, Bond RV Metrics, Stochastic Evolution and Optio…

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.

A library for high-level algorithmic differentiation

Material Definition with Automatic Differentiation

Algorithmic differentiation with hyper-dual numbers in C++ and Python

Fast risks with QuantLib in C++

mirror of Infergo repository

Various developer materials, like PDFs, notes, derivations, etc. for differential equations and scientific machine learning (SciML)

Math on (Hyper-Dual) Tensors with Trailing Axes

Matrix derivative tests for algorithmic differentiation

A simple, pure python algorithmic differentiation package

Automatic differentiation (a.k.a algorithmic differentiation) in reverse mode for elm

Demonstrator codes for MPI parallel taping and interpretation

Mirror of bitbucket infergo-studies repository

Hyperelastic formulations using an algorithmic differentiation with hyper-dual numbers in Python.