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From Model Calibration and Validation to Reliable Extrapolations

  • Gabriel TerejanuEmail author
Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)

Abstract

The purpose of this paper is to propose and explore a validation and predictive assessment process that supports extrapolative predictions for models with known sources of error. In general, the ultimate purpose of most computational models is to make predictions, which are not experimentally observable. As a result assessing the validity of extrapolative predictions is more challenging than situations encountered in classical validation, where model outputs for observed quantities are compared to observations. In this paper a comprehensive approach is proposed to justify extrapolative predictions for models with known sources of error. The connection between model calibration, validation and prediction is made through the introduction of alternative uncertainty models used to model the localized errors. In addition a prediction assessment tool is introduced to characterize the reliability of model predictions in the absence of data. The proposed methodology is applied to an illustrative extrapolation problem involving a misspecified nonlinear oscillator.

Keywords

Prediction validation Model error Discrepancy model Structural uncertainty Bayesian inference 

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Copyright information

© The Society for Experimental Mechanics, Inc. 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringUniversity of South CarolinaColumbiaUSA

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