Article2011

Quantifying Multiple Types of Uncertainty in Physics-Based Simulation Using Bayesian Model Averaging

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Probabilistic and Robust Engineering Design Control Systems and Identification Gaussian Processes and Bayesian Inference

Abstract

Given experimental data measured from an engineering system, response predictions by a stochastic simulation model involve both parametric uncertainty and random errors. Also, model form uncertainty arises when two or more simulation models predict the responses of an engineering system because it is beyond capability to identify the best approximating model among the considered model set. In this research, a methodology is developed to quantify model probability using measured deviations between experimental data and model predictions of the data under a Bayesian statistical framework. Model averaging is used to combine the predictions of a system response by a model set into a single prediction. Then, a nonlinear spring-mass system is used to demonstrate the process for implementing model averaging. Finally, the methodology is applied to the engineering benefits of a laser peening process, and a confidence band for a residual stress field is established to indicate the reliability of the composite prediction of the stress field.

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References

2007A Framework for Validation of Computer Models 2004Challenge problems: uncertainty in system response given uncertain parameters 1996Two methods for the structured assessment of model uncertainty by experts in performance assessments of radioactive waste repositories 2010A Bayesian approach for quantification of model uncertainty 2008A better understanding of model updating strategies in validating engineering models 2004Model Validation via Uncertainty Propagation and Data Transformations 2010Quantification of model-form and predictive uncertainty for multi-physics simulation

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