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

Practical Applications of Gaussian Process with Uncertainty Quantification and Sensitivity Analysis for Digital Twin for Accident Tolerant Fuel