  To understand the potential of intelligent confirmatory tools, the U.S.
Nuclear Regulatory Committee (NRC) initiated a future-focused research project
to assess the regulatory viability of machine learning (ML) and artificial
intelligence (AI)-driven Digital Twins (DTs) for nuclear power applications.
Advanced accident tolerant fuel (ATF) is one of the priority focus areas of the
U.S. Department of Energy (DOE). A DT framework can offer game-changing yet
practical and informed solutions to the complex problem of qualifying advanced
ATFs. Considering the regulatory standpoint of the modeling and simulation
(M&S) aspect of DT, uncertainty quantification and sensitivity analysis are
paramount to the DT framework's success in terms of multi-criteria and
risk-informed decision-making. This chapter introduces the ML-based uncertainty
quantification and sensitivity analysis methods while exhibiting actual
applications to the finite element-based nuclear fuel performance code BISON.
