A usability case study of algorithmic differentiation tools on the ISSM ice sheet model
Algorithmic differentiation (AD) based on operator overloading is often the only feasible
approach for applying AD in complex C++ software environments. Challenges pertaining to …
approach for applying AD in complex C++ software environments. Challenges pertaining to …
[PDF][PDF] Comparison of two gradient computation methods in Python
Gradient based optimization and machine learning applications require the computation of
derivatives. For example, artificial neural networks (ANNs), a widely used learning system …
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) …
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 …
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 …
dimensional state or parameter spaces in an efficient manner is of considerable interest in …