Error and Uncertainty Analysis of Inexact and Imprecise Computer Models
Computer simulations are routinely executed to predict the behavior of complex systems in many fields of engineering and science. These computer-aided predictions involve the theoretical foundation, numerical modeling, and supporting experimental data, all of which come with their associated errors. A natural question then arises concerning the validity of computer model predictions, especially in cases where these models are executed in support of high-consequence decision making. This article lays out a methodology for quantifying the degrading effects of incompleteness and inaccuracy of the theoretical foundation, numerical modeling, and experimental data on the computer model predictions. Through the method discussed in this paper, the validity of model predictions can be judged and communicated between involved parties in a quantitative and objective manner.
errors computer models validation optimization uncertainty principles parameters calibration computer aided simulation uncertainty propagation discrepancy bias parameter calibration bias correction model validation model form error test-analysis correlation