BibTeX
@ARTICLE{
         Mischler1995ADa,
       title = "Automatic Differentiation applications to computer aided process engineering",
       journal = "Computers \& Chemical Engineering",
       volume = "19",
       number = "Supplement 1",
       pages = "779--784",
       year = "1995",
       note = "European Symposium on Computer Aided Process Engineering 3-5",
       issn = "0098-1354",
       doi = "DOI: 10.1016/0098-1354(95)87129-2",
       url =
         "http://www.sciencedirect.com/science/article/B6TFT-48MRDFW-4G/2/d150752bdfebaba7bf157cb143e154b9",
       author = "C. Mischler and X. Joulia and E. Hassold and A. Galligo and R. Esposito",
       keywords = "Automatic Differentiation,forward mode,reverse mode,thermodynamic properties",
       abstract = "The numerical methods used for solving many scientific computing problems, in
         particular, for computer aided process engineering, require the computation of derivatives of a
         function. Both the accuracy and the computational requirements of the derivatives are, usually, of
         critical importance to ensure the robustness and the efficiency of the numerical solution. The
         purpose of this paper is to report on the development and application of computer algebra systems
         for automating the implementation of differentiation into existing computer codes. The so-called
         Automatic Differentiation (AD) Methods have been tested on several examples of thermodynamic
         property calculation routines. We have used the Odyssée system, developed for the project
         SAFIR. The various automatic differentiation algorithms, called forward (direct) mode and reverse
         (adjoint) mode, have been compared on these examples. Our results show that Automatic
         Differentiation algorithms can handle real- life codes and that the codes generated are competitive
         in time with calculation of derivatives by divided-difference approximations. For some classes of
         programs, the codes generated are much more efficient so as to be competitive with handmade
         derivatives. Moreover, Automatic Differentiation avoids the truncation errors found in finite
         difference approximation calculation and also the tedious (and error prone) analytical derivation
         work done by hand.",
       ad_area = "Process Engineering",
       ad_tools = "Odyssee"
}