GRESS

GRESS, a preprocessor for sensitivity analysis of Fortran programs. Sensitivity and uncertainty analyses are necessary components in the evaluation of computer simulation models. The identification of important model parameters is valuable for ascertaining specific data needs and as a first step in a thorough assessment of parameter uncertainties. This report describes an automated procedure for performing a comprehensive sensitivity analysis using computer calculus. The procedure employs an automated system called GRESS, which utilizes a precompiler to enhance a Fortran computer code by adding derivative-taking capabilities. From a single run of an enhanced model, GRESS calculates normalized sensitivities and derivatives of selected results with respect to all input data. GRESS computes a normalized sensitivity by multiplying a derivative by its associated input parameter value and dividing by the associated output value. Sensitivities can be reported or used during execution of the enhanced code. Benchmark results demonstrate that an automated procedure can be used cheaply and efficiently to perform a comprehensive sensitivity analysis of existing computer models.


References in zbMATH (referenced in 12 articles , 1 standard article )

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  1. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
  2. Fang, F.; Pain, C. C.; Navon, I. M.; Cacuci, D. G.; Chen, X.: The independent set perturbation method for efficient computation of sensitivities with applications to data assimilation and a finite element shallow water model (2013)
  3. Papadimitriou, Dimitrios I.; Giannakoglou, Kyriakos C.: Aerodynamic shape optimization using first and second order adjoint and direct approaches (2008)
  4. Barhen, Jacob; Protopopescu, Vladimir; Reister, David B.: Consistent uncertainty reduction in modeling nonlinear systems (2004)
  5. Bischof, Christian H.; Bücker, H. Martin; Wu, Po-Ting: Time-parallel computation of pseudo-adjoints for a leapfrog scheme (2004)
  6. Baum, Shari R.: Age differences in the influence of metrical structure on phonetic identification (2003)
  7. Martins, Joaquim R. R. A.; Sturdza, Peter; Alonso, Juan J.: The complex-step derivative approximation (2003)
  8. Griewank, Andreas; Juedes, David; Utke, Jean: Algorithm 755: ADOL-C: A package for the automatic differentiation of algorithms written in C/C++ (1996)
  9. Rich, Lawrence C.; Hill, David R.: Automatic differentiation in MATLAB (1992)
  10. Horwedel, Jim E.: GRESS, a preprocessor for sensitivity analysis of Fortran programs (1991)
  11. Juedes, David W.: A taxonomy of automatic differentiation tools (1991)
  12. Worley, Brian A.: Experience with the forward and reverse mode of GRESS in contaminent transport modeling and other applications (1991)