Inverse uncertainty quantification based on the modular Bayesian approach

Uncertainty quantification (UQ) is an essential step in computational model validation because assessment of the model accuracy requires a concrete, quantifiable measure of uncertainty in the model predictions. The concept of UQ in the nuclear community generally means forward UQ (FUQ), in which the information flow is from the inputs to the outputs. Inverse UQ (IUQ), in which the information flow is from the model outputs and experimental data to the inputs, is an equally important component of UQ but has been significantly underrated until recently. FUQ requires knowledge of the input uncertainties, which has been widely specified by expert opinion or user self-evaluation. IUQ is the process of inversely quantifying the input uncertainties based on experimental data. Multiple IUQ methods have been developed in the nuclear community to quantify the input uncertainties in the physical models of system thermal-hydraulics codes. In this chapter, a brief overview of the IUQ methods will be provided. We will also introduce the Modular Bayesian Approach which has a very comprehensive formulation for IUQ.