Publication
Title
FastDer++, efficient automatic differentiation for non-linear PDE solvers
Author
Abstract
FastDer++ is a C++ class library for automatic differentiation designed for use in situations where a set of dependent variables and their gradients are to be evaluated in a large number of points. Typical settings constitute non-linear systems of partial differential equations (PDEs) and ODEs. Although automatic differentiation is traditionally considered to slow for implementation in non-linear PDE and ODE solvers, it has recently been demonstrated [E. Tijskens, H. Ramon, J. De Baerdemaeker, Efficient operator overloading AD for solving non-linear PDEs, in: G. Corliss, C. Faure, A. Griewank, L. Hascoet, U. Nauman (Eds.), Automatic Differentiation of Algorithms-From Simulation to Optimisation, Springer, Verlag, 2002; Num. Algorithms 30 (2002) 259] that thanks to an extension called vectorised AD and careful design handcoded derivatives, finite differencing and state of the art AD tools can be outperformed in common situations. In addition, the user gains the advantage of directly dealing with the non-linear equations rather than with its linearised counterpart. This paper describes the FastDer++ library and its underlying principles in detail, both from the point of implementation and of user programming. (C) 2003 IMACS. Published by Elsevier B.V. All rights reserved.
Language
English
Source (journal)
Mathematics and computers in simulation. - Amsterdam
Publication
Amsterdam : 2004
ISSN
0378-4754
Volume/pages
65:1-2(2004), p. 177-190
ISI
000221239900018
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Publication type
Subject
External links
Web of Science
Record
Identification
Creation 24.02.2014
Last edited 08.08.2017