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Application of Higher Order Derivatives to Parameterization

  • Jean-Daniel Beley
  • Stephane Garreau
  • Frederic Thevenon
  • Mohamed Masmoudi
Chapter

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.

Keywords

Computational Fluid Dynamic High Order Derivative Automatic Differentiation Parameterization Tool Partial Differential Equation Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  • Jean-Daniel Beley
  • Stephane Garreau
  • Frederic Thevenon
  • Mohamed Masmoudi

There are no affiliations available

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