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New Bayesian Updating Methodology for Model Validation and Robust Predictions Based on Data from Hierarchical Subsystem Tests

Year: 2008
Type: article
Abstract: In many engineering applications, it is a formidable task to construct a mathematical model \nthat is expected to produce accurate predictions of the behavior of a system of interest. \nDuring the construction of such predictive models, errors due to imperfect modeling and \nuncertainties due to incomplete information about the system and its input always exist and \ncan be accounted for appropriately by using probability logic. Often one has to decide \nwhich proposed candidate models are acceptable for prediction of the target system \nbehavior. In recent years, the problem of developing an effective model validation \nmethodology has attracted attention in many different fields of engineering and applied \nscience. Here, we consider the problem where a series of experiments are conducted that \ninvolve collecting data from successively more complex subsystems and these data are to \nbe used to predict the response of a related more complex system. A novel methodology \nbased on Bayesian updating of hierarchical stochastic system model classes using such \nexperimental data is proposed for uncertainty quantification and propagation, model \nvalidation, and robust prediction of the response of the target system. After each test stage, \nwe use all the available data to calculate the posterior probability of each stochastic system \nmodel along with the quality of its robust prediction. The proposed methodology is applied \nto the 2006 Sandia static-frame validation challenge problem to illustrate our approach for \nmodel validation and robust prediction of the system response. Recently-developed \nstochastic simulation methods are used to solve the computational problems involved.
Language: English
Open Access status: green
OpenAlex ID: W2163318190
FWCI: 2.122
Cites: 33
Cited by: 15
Related to: 10