Statistical Validation of Engineering and Scientific Models: Validation Experiments to Application
Year: 2003
Type: report
Abstract: Several major issues associated with model validation are addressed here. First, we extend the application-based, model validation metric presented in Hills and Trucano (2001) to the Maximum Likelihood approach introduced in Hills and Trucano (2002). This method allows us to use the target application of the code to weigh the measurements made from a validation experiment so that those measurements that are most important for the application are more heavily weighted. Secondly, we further develop the linkage between suites of validation experiments and the target application so that we can (1) provide some measure of coverage of the target application and, (2) evaluate the effect of uncertainty in the measurements and model parameters on application level validation. We provide several examples of this approach based on steady and transient heat conduction, and shock physics applications.
DOI: 10.2172/809991
Authors: RICHARD HILLS, IAN LESLIE
Institution: New Mexico State University
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W2107469042