Publication: Using Automatic Differentiation to Study the Sensitivity of a Crop Model
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Using Automatic Differentiation to Study the Sensitivity of a Crop Model

- incollection -
 

Author(s)
Claire Lauvernet , Laurent Hascoët , François-Xavier Le Dimet , Frédéric Baret

Published in
Recent Advances in Algorithmic Differentiation

Editor(s)
Shaun Forth, Paul Hovland, Eric Phipps, Jean Utke, Andrea Walther

Year
2012

Publisher
Springer

Abstract
Automatic Differentiation (ad) is often applied to codes that solve partial differential equations, e.g. in geophysical sciences or Computational Fluid Dynamics. In agronomy, the differentiation of crop models has never been performed since these models are more empirical than fully mecanistic, derived from equations. This study shows the feasibility of constructing the adjoint model of a crop model referent in the agronomic community (STICS) with the TAPENADE tool, and the use of this accurate adjoint to perform some sensitivity analysis. This paper reports on the experience from ad users of the environmental domain, in which ad usage is not very widespread.

Cross-References
Forth2012RAi

AD Tools
TAPENADE

BibTeX
@INCOLLECTION{
         Lauvernet2012UAD,
       title = "Using Automatic Differentiation to Study the Sensitivity of a Crop Model",
       doi = "10.1007/978-3-642-30023-3_6",
       author = "Claire Lauvernet and Laurent Hasco{\"e}t and Fran\c{c}ois-Xavier Le
         Dimet and Fr{\'e}d{\'e}ric Baret",
       abstract = "Automatic Differentiation (AD) is often applied to codes that solve partial
         differential equations, e.g. in geophysical sciences or Computational Fluid Dynamics. In agronomy,
         the differentiation of crop models has never been performed since these models are more empirical
         than fully mecanistic, derived from equations. This study shows the feasibility of constructing the
         adjoint model of a crop model referent in the agronomic community (STICS) with the TAPENADE tool,
         and the use of this accurate adjoint to perform some sensitivity analysis. This paper reports on the
         experience from AD users of the environmental domain, in which AD usage is not very widespread.",
       pages = "59--69",
       crossref = "Forth2012RAi",
       booktitle = "Recent Advances in Algorithmic Differentiation",
       series = "Lecture Notes in Computational Science and Engineering",
       publisher = "Springer",
       address = "Berlin",
       volume = "87",
       editor = "Shaun Forth and Paul Hovland and Eric Phipps and Jean Utke and Andrea Walther",
       isbn = "978-3-540-68935-5",
       issn = "1439-7358",
       year = "2012",
       ad_tools = "TAPENADE"
}


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