Efficient Greek calculation using Adjoint Algorithmic Differentiation

This repository contains a software for performing optimized Dense Hessian Chain Bracketing.

The works focus on pruning a neural network, structurally, based on the sensitivity of weights that cover the entire output range of the loss function..

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

Towards Sobolev Pruning (PASC'24 Conference Paper)

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

Differentiable Tensors based on NumPy Arrays

Matrix derivative tests for algorithmic differentiation

A simple, pure python algorithmic differentiation package

Demonstrator codes for MPI parallel taping and interpretation

Mirror of bitbucket infergo-studies repository

mirror of Infergo repository

Material Definition with Automatic Differentiation

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

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

A library for high-level algorithmic differentiation

Automates steady and unsteady adjoints (general solvers and ODEs respectively). 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.

Fast risks with QuantLib in C++

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…

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