Publication: A Symbolic Code Generator for Parameter Estimation
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A Symbolic Code Generator for Parameter Estimation

- incollection -
 

Author(s)
Guy Chavent , Jérôme Jaffré , Sophie Jégou , Jun Liu

Published in
Computational Differentiation: Techniques, Applications, and Tools

Editor(s)
Martin Berz, Christian Bischof, George Corliss, Andreas Griewank

Year
1996

Publisher
SIAM

Abstract
GRADJ, a symbolic code generator (SCG), based on the Symbolic Computation System Maple, is described. Given discretized PDE's and a discrete data misfit function, it generates two Fortran codes. One computes the data misfit function, and the other computes its gradient. The performance of the generated codes for estimating parameters for flow in porous media is examined.

Cross-References
Berz1996CDT

BibTeX
@INCOLLECTION{
         Chavent1996ASC,
       author = "Guy Chavent and J{\'e}r{\^o}me Jaffr{\'e} and Sophie
         J{\'e}gou and Jun Liu",
       editor = "Martin Berz and Christian Bischof and George Corliss and Andreas Griewank",
       title = "A Symbolic Code Generator for Parameter Estimation",
       booktitle = "Computational Differentiation: Techniques, Applications, and Tools",
       pages = "129--136",
       publisher = "SIAM",
       address = "Philadelphia, PA",
       key = "Chavent1996ASC",
       crossref = "Berz1996CDT",
       abstract = "GRADJ, a symbolic code generator (SCG), based on the Symbolic Computation System
         Maple, is described. Given discretized PDE's and a discrete data misfit function, it generates
         two Fortran codes. One computes the data misfit function, and the other computes its gradient. The
         performance of the generated codes for estimating parameters for flow in porous media is examined.",
       keywords = "Parameter estimation, symbolic code generator, Maple, adjoint state method, flow in
         porous media.",
       year = "1996"
}


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