Inverse uncertainty quantification based on the modular Bayesian approach

Research output: Chapter in Book/Report/Conference proceedingChapter


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.

Original languageEnglish (US)
Title of host publicationRisk-informed Methods and Applications in Nuclear and Energy Engineering
Subtitle of host publicationModeling, Experimentation, and Validation
Number of pages13
ISBN (Electronic)9780323911528
ISBN (Print)9780323998185
StatePublished - Jan 1 2023


  • Bayesian calibration
  • Inverse UQ
  • Modular Bayesian Approach
  • Surrogate modeling
  • Thermal-hydraulics
  • Uncertainty quantification (UQ)

ASJC Scopus subject areas

  • General Physics and Astronomy


Dive into the research topics of 'Inverse uncertainty quantification based on the modular Bayesian approach'. Together they form a unique fingerprint.

Cite this