TY - GEN
T1 - Comparative study of uncertainty quantification metrics via a stochastic method of model validation
AU - Bi, Sifeng
AU - Atamturktur, Sez
AU - Deng, Zhongmin
N1 - Funding Information:
Support for this research was provided by the China Scholarship Council, which is appreciated.
Publisher Copyright:
© The Society for Experimental Mechanics, Inc. 2014.
PY - 2014
Y1 - 2014
N2 - Uncertainty quantification metrics provide a quantitative measure of the agreement between predictions and observations. These metrics not only significantly influence the outcomes of model calibration but also provide a means of determining the desired level of fidelity for model validation. This manuscript evaluates the influence of these uncertainty quantification metrics on model validation focusing on Euclidian distance, i.e. the absolute geometric distance between two points; and Mahalanobis distance, i.e. the weighted distance between a point and a population that considers the correlations and Bhattacharyya distance, i.e. the weighted distance between two populations that considers the correlations. Discussions are provided on the use of these three metrics when comparing model predictions against observations in the context of model calibration and validation. These metrics are implemented and examined via a model validation method based on Monte Carlo and stochastic test-analysis correlation techniques. A finite element model of a frame structure with a set of uncertain parameters is provided in the simulated example to demonstrate these ideas.
AB - Uncertainty quantification metrics provide a quantitative measure of the agreement between predictions and observations. These metrics not only significantly influence the outcomes of model calibration but also provide a means of determining the desired level of fidelity for model validation. This manuscript evaluates the influence of these uncertainty quantification metrics on model validation focusing on Euclidian distance, i.e. the absolute geometric distance between two points; and Mahalanobis distance, i.e. the weighted distance between a point and a population that considers the correlations and Bhattacharyya distance, i.e. the weighted distance between two populations that considers the correlations. Discussions are provided on the use of these three metrics when comparing model predictions against observations in the context of model calibration and validation. These metrics are implemented and examined via a model validation method based on Monte Carlo and stochastic test-analysis correlation techniques. A finite element model of a frame structure with a set of uncertain parameters is provided in the simulated example to demonstrate these ideas.
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U2 - 10.1007/978-3-319-04546-7_27
DO - 10.1007/978-3-319-04546-7_27
M3 - Conference contribution
AN - SCOPUS:84988695772
SN - 9783319007762
T3 - Conference Proceedings of the Society for Experimental Mechanics Series
SP - 235
EP - 243
BT - Mechanics of Biological Systems and Materials - Proceedings of the 2013 Annual Conference on Experimental and Applied Mechanics
PB - Springer New York LLC
T2 - 32nd IMAC Conference and Exposition on Structural Dynamics, 2014
Y2 - 3 February 2014 through 6 February 2014
ER -