Real-world problems often involve complex systems that cannot be perfectly modeled or identified, and many engineering applications aim to design operators that can perform reliably in the presence of such uncertainty. When quantifying the uncertainty of a complex system, it is practically beneficial to quantify the expected impact that this uncertainty has on the operational goal rather than the uncertainty per se.

Recently, a novel Bayesian framework has been proposed for objective-based uncertainty quantification (UQ), which quantifies the uncertainty in a given system based on the expected increase of the operational cost that it induces. This measure of uncertainty, called MOCU (mean objective cost of uncertainty), provides a practical way of quantifying the effect of various types of system uncertainties on the operation of interest. Furthermore, the MOCU-based UQ framework provides a general mathematical basis for integrating prior system knowledge and available data, designing robust operators, and designing optimal experiments that can effectively reduce the uncertainty.

In this website we present the latest research findings relevant to objective-based UQ (or simply objective UQ) and provide a compilation of papers and resources relevant to MOCU and OED, and more broadly, the Bayesian paradigm that enables integration & utilization of prior knowledge and data and also enables effective operational & experimental design under uncertainty.

Website managed by: Byung-Jun Yoon and Xiaoning Qian