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Analysis of Multi-domain Complex Simulation Studies

  • James R. Gattiker
  • Earl Lawrence
  • David Higdon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3982)

Abstract

Complex simulations are increasingly important in systems analysis and design. In some cases simulations can be exhaustively validated against experiment and taken to be implicitly accurate. However, in domains where only limited validation of the simulations can be performed, implications of simulation studies have historically been qualitative. Validation is notably difficult in cases where experiments are expensive or otherwise prohibitive, where experimental effects are difficult to measure, and where models are thought to have unaccounted systematic error. This paper describes an approach to integrate simulation experiments with empirical data that has been applied successfully in a number of domains. This methodology generates coherent estimates of confidence in model predictions, model parameters, and estimates, i.e. calibrations, for unobserved variables. Extensions are described to integrate the results of separate experiments into a single estimate for simulation parameters, which demonstrates a new approach to model-based data fusion.

Keywords

Simulation Response Gravitational Attraction Thermal Activation Energy Variable Importance Measure Maximum Yield Stress 
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 2006

Authors and Affiliations

  • James R. Gattiker
    • 1
  • Earl Lawrence
    • 1
  • David Higdon
    • 1
  1. 1.Los Alamos National Laboratory 

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