A usability case study of algorithmic differentiation tools on the ISSM ice sheet model

A Hück, C Bischof, M Sagebaum… - Optimization Methods …, 2018 - Taylor & Francis
Algorithmic differentiation (AD) based on operator overloading is often the only feasible
approach for applying AD in complex C++ software environments. Challenges pertaining to …

[PDF][PDF] Comparison of two gradient computation methods in Python

SHK Narayanan, P Hovland, K Kulshreshtha… - 2017 - drive.google.com
Gradient based optimization and machine learning applications require the computation of
derivatives. For example, artificial neural networks (ANNs), a widely used learning system …

[HTML][HTML] Compiler support for operator overloading and algorithmic differentiation in c++

A Hück - 2020 - tuprints.ulb.tu-darmstadt.de
Multiphysics software needs derivatives for, eg, solving a system of non-linear equations,
conducting model verification, or sensitivity studies. In C++, algorithmic differentiation (AD) …

[PDF][PDF] Interfacing source transformation AD with operator overloading libraries

K Kulshreshtha, SHK Narayanan - 2016 - osti.gov
Scientific applications are usually written in a single language such as C, C++, or a flavor of
Fortran. Various algorithmic differentiation (AD) tools exist to differentiate these applications …

[PDF][PDF] Algorithmic Differentiation for Climate Science

Computing sensitivities of climate-related quantities of interest (QoI) to very high-
dimensional state or parameter spaces in an efficient manner is of considerable interest in …