  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 in the input uncertainties
which has been specified by expert opinion or user self-evaluation. IUQ is
defined as the process to inversely quantify the input uncertainties based on
experimental data. This review paper aims to provide a comprehensive and
comparative discussion of the major aspects of the IUQ methodologies that have
been used on the physical models in system thermal-hydraulics codes. IUQ
methods can be categorized by three main groups: frequentist (deterministic),
Bayesian (probabilistic), and empirical (design-of-experiments). We used eight
metrics to evaluate an IUQ method, including solidity, complexity,
accessibility, independence, flexibility, comprehensiveness, transparency, and
tractability. Twelve IUQ methods are reviewed, compared, and evaluated based on
these eight metrics. Such comparative evaluation will provide a good guidance
for users to select a proper IUQ method based on the IUQ problem under
investigation.
