Abstract
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 distance-based 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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.