  The application of digital twin (DT) technology to the nuclear field is one
of the challenges in the future development of nuclear energy. Possible
applications of DT technology in the nuclear field are expected to be very
wide: operate commercial nuclear reactors, monitor spent fuel storage and
disposal facilities, and develop new nuclear systems. As U.S. Nuclear
Regulatory Committee (NRC) recently announced, machine learning (ML) and
artificial intelligence (AI) will be new domains in the nuclear field.
Considering the data science perspective, Gaussian Process (GP) has proven to
be an ML algorithm for modeling and simulation components of the digital twin
framework, specifically for the accident tolerant fuel (ATF) concepts. ATF is
one of the high-priority areas for both the U.S. Department of Energy (DOE) and
NRC. GP's inherent treatment of lack of data, missing data, and data
inconsistencies (noisy/erroneous data) present in the ATF concepts make it an
attractive machine learning algorithm for implementation in the DT framework.
This chapter focuses on the practical demonstration of GP and its applicability
to the DT framework for predicting ATF.
