Abstract
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.
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.