Certified Interval Model Updating Using Scenario Optimization

Aerospace design relies on computational models to manage the scarcity of test data and to quantify various input parameters. Still, the reliability of these estimates with respect to their robustness against unseen data remains uncertain. To address this, the present study introduces certified interval model updating using scenario optimization. This approach determines a lower bound for this reliability against missing data without having to characterize the underlying distribution of the data. Additionally, it allows calculating the required amount of data to achieve a predefined lower bound reliability before data gathering. Furthermore, scenario optimization allows for an efficient forward propagation of the interval uncertainty. The method’s efficacy is illustrated using a simple four-degree-of-freedom mass–spring system, and its performance is benchmarked against other interval model-updating techniques using the DLR AIRMOD case.