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Using Automatic Differentiation to study the sensitivity of a crop model

Abstract : Automatic Differentiation methods are often applied to codes that solve partial differential equations, e.g. in the domains of geophysical sciences, such as meteorology or oceanography, or Computational Fluid Dynamics. In agronomy, the differentiation of crop model has never been performed since the models are not fully deterministic but much more empirical. This study shows the feasability of constructing the adjoint model of a crop model referent in the agronomic community (STICS) with the TAPENADE tool, and the use of this adjoint to perform some robust sensitivity analysis. This aims at giving a return of experience from users working in the environmental thematic, and presents a somewhat unusual field of application of Automatic Differentiation.
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Submitted on : Monday, December 17, 2012 - 4:41:33 PM
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  • HAL Id : hal-00766141, version 1
  • IRSTEA : PUB00035563

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Claire Lauvernet, Laurent Hascoet, François-Xavier Le Dimet, Frédéric Baret. Using Automatic Differentiation to study the sensitivity of a crop model. Lecture Notes in Computational Science and Engineering, Springer, 2012, 87, p. 59 - p. 69. ⟨hal-00766141⟩

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