TY - JOUR
T1 - Uncertainty quantification metrics with varying statistical information in model calibration and validation
AU - Bi, Sifeng
AU - Prabhu, Saurabh
AU - Cogan, Scott
AU - Atamturktur, Sez
N1 - Publisher Copyright:
© Copyright 2017 by S. Bi, S. Prabhu, S. Cogan, and S. Atamturktur.
PY - 2017
Y1 - 2017
N2 - Test-analysis comparison metrics are mathematical functions that provide a quantitative measure of the agreement (or lack thereof) between numerical predictions and experimental measurements. While calibrating and validating models, the choice of a metric can significantly influence the outcome, yet the published research discussing the role of metrics, in particular, varying levels of statistical information the metrics can contain, has been limited. This paper calibrates and validates the model predictions using alternative metrics formulated based on three types of distancebased criteria: 1) Euclidian distance (i.e., the absolute geometric distance between two points), 2) Mahalanobis distance (i.e., the weighted distance that considers the correlations of two point clouds), and 3) Bhattacharyya distance (i.e., the statistical distance between two point clouds considering their probabilistic distributions). A comparative study is presented in the first case study, where the influence of various metrics, and the varying levels of statistical information they contain, on the predictions of the calibrated models is evaluated. In the second case study, an integrated application of the distance metrics is demonstrated through a cross-validation process with regard to the measurement variability.
AB - Test-analysis comparison metrics are mathematical functions that provide a quantitative measure of the agreement (or lack thereof) between numerical predictions and experimental measurements. While calibrating and validating models, the choice of a metric can significantly influence the outcome, yet the published research discussing the role of metrics, in particular, varying levels of statistical information the metrics can contain, has been limited. This paper calibrates and validates the model predictions using alternative metrics formulated based on three types of distancebased criteria: 1) Euclidian distance (i.e., the absolute geometric distance between two points), 2) Mahalanobis distance (i.e., the weighted distance that considers the correlations of two point clouds), and 3) Bhattacharyya distance (i.e., the statistical distance between two point clouds considering their probabilistic distributions). A comparative study is presented in the first case study, where the influence of various metrics, and the varying levels of statistical information they contain, on the predictions of the calibrated models is evaluated. In the second case study, an integrated application of the distance metrics is demonstrated through a cross-validation process with regard to the measurement variability.
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U2 - 10.2514/1.J055733
DO - 10.2514/1.J055733
M3 - Article
AN - SCOPUS:85030870656
SN - 0001-1452
VL - 55
SP - 3570
EP - 3583
JO - AIAA journal
JF - AIAA journal
IS - 10
ER -