Publication: Application of Higher Order Derivatives to Parameterization
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Application of Higher Order Derivatives to Parameterization

- incollection -
 

Author(s)
Jean-Daniel Beley , Stephane Garreau , Frederic Thevenon , Mohamed Masmoudi

Published in
Automatic Differentiation of Algorithms: From Simulation to Optimization

Editor(s)
George Corliss, Christèle Faure, Andreas Griewank, Laurent Hascoët, Uwe Naumann

Year
2002

Publisher
Springer

Abstract
Research on automatic differentiation is mainly motivated by gradient computation and optimization. However, in the optimal design area, it is quite difficult to use optimization tools. Some constraints (e.g. aesthetics constraints, manufacturing constraints) are quite difficult to describe by mathematical expressions. In practice, the optimal design process is a dialog between the designer and the analysis software (structural analysis, electromagnetism, computational fluid dynamics, etc.). One analysis may take a while. Hence, parameterization tools such as design of experiments (D.O.E.) and neural networks are used. The aim of those tools is to build surrogate models. We present a parameterization method based on higher order derivatives computation obtained by automatic differentiation.

Cross-References
Corliss2002ADo

BibTeX
@INCOLLECTION{
         Beley2002AoH,
       author = "Jean-Daniel Beley and Stephane Garreau and Frederic Thevenon and Mohamed Masmoudi",
       title = "Application of Higher Order Derivatives to Parameterization",
       pages = "335--341",
       chapter = "40",
       crossref = "Corliss2002ADo",
       booktitle = "Automatic Differentiation of Algorithms: From Simulation to Optimization",
       year = "2002",
       editor = "George Corliss and Christ{\`e}le Faure and Andreas Griewank and Laurent
         Hasco{\"e}t and Uwe Naumann",
       series = "Computer and Information Science",
       publisher = "Springer",
       address = "New York, NY",
       abstract = "Research on automatic differentiation is mainly motivated by gradient computation
         and optimization. However, in the optimal design area, it is quite difficult to use optimization
         tools. Some constraints (e.g. aesthetics constraints, manufacturing constraints) are quite difficult
         to describe by mathematical expressions. In practice, the optimal design process is a dialog between
         the designer and the analysis software (structural analysis, electromagnetism, computational fluid
         dynamics, etc.). One analysis may take a while. Hence, parameterization tools such as design of
         experiments (D.O.E.) and neural networks are used. The aim of those tools is to build surrogate
         models. We present a parameterization method based on higher order derivatives computation obtained
         by automatic differentiation."
}


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