J
Jean Utke
Researcher at Argonne National Laboratory
Publications - 56
Citations - 1755
Jean Utke is an academic researcher from Argonne National Laboratory. The author has contributed to research in topics: Automatic differentiation & Computer science. The author has an hindex of 15, co-authored 47 publications receiving 1676 citations. Previous affiliations of Jean Utke include University of Chicago & Dresden University of Technology.
Papers
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Journal ArticleDOI
Algorithm 755: ADOL-C: a package for the automatic differentiation of algorithms written in C/C++
TL;DR: The C++ package ADOL-C described here facilitates the evaluation of first and higher derivatives of vector functions that are defined by computer programs written in C or C++.
Journal ArticleDOI
OpenAD/F: A Modular Open-Source Tool for Automatic Differentiation of Fortran Codes
Jean Utke,Uwe Naumann,Mike Fagan,Nathan R. Tallent,Michelle Mills Strout,Patrick Heimbach,Chris Hill,Carl Wunsch +7 more
TL;DR: The Open/ADF tool allows the evaluation of derivatives of functions defined by a Fortran program, and supports various code reversal schemes with hierarchical checkpointing at the subroutine level for the generation of adjoint codes.
BookDOI
Advances in Automatic Differentiation
TL;DR: This collection covers advances in automatic differentiation theory and practice and discusses various applications, which provide insight into effective strategies for using automatic differentiation for inverse problems and design optimization.
Journal ArticleDOI
Evaluating higher derivative tensors by forward propagation of univariate Taylor series
TL;DR: With the approach presented, much simpler data access patterns and similar or lower computational counts can be achieved through propagating a family of univariate Taylor series of a suitable degree.
Journal ArticleDOI
Timescales and regions of the sensitivity of Atlantic meridional volume and heat transport: Toward observing system design
TL;DR: In this article, the authors presented a model for estimating the Circulation and Climate of the Ocean (COC) and the Atlantic MOC Observing System Studies Using Adjoint Models.