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Uncertainty Analysis Based on Sensitivities Generated Using Automatic Differentiation

  • Jacob Barhen
  • David B. Reister
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2668)

Abstract

The objective is to determine confidence limits for the outputs of a mathematical model of a physical system that consists of many interacting computer codes. Each code has many modules that receive inputs, write outputs, and depend on parameters. Several of the outputs of the system of codes can be compared to sensor measurements. The outputs of the system are uncertain because the inputs and parameters of the system are uncertain. The method uses sensitivities to propagate uncertainties from inputs to outputs through the complex chain of modules. Furthermore, the method consistently combines sensor measurements with model outputs to simultaneously obtain best estimates for model parameters and reduce uncertainties in model outputs. The method was applied to a test case where ADIFOR2 was used to calculate sensitivities for the radiation transport code MODTRAN.

Keywords

Covariance Matrix Probability Density Function Uncertainty Analysis Sensor Measurement Automatic Differentiation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Jacob Barhen
    • 1
  • David B. Reister
    • 1
  1. 1.Oak Ridge National LaboratoryOak Ridge

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