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Nuclear power is a key electricity source, with light water reactors being the most common type. Their fuel typically consists of uranium dioxide pellets stacked in zirconium alloy cladding tubes. The purpose of the fuel is to produce heat and act as a barrier against releasing radioactive material. During operation, heat and radiation cause thermomechanical changes that can lead to fuel failure if not controlled. Thus, the nuclear industry needs efficient fuel performance codes with well-quantified uncertainties to predict fuel rod behavior. This thesis focuses on improving inverse uncertainty quantification and surrogate modeling for efficient fuel performance predictions.

Inverse uncertainty quantification is essential because fuel performance codes require calibrated model parameters to ensure that predictions match measurements. However, standard calibration methods often underestimate uncertainties due to unaccounted-for uncertainty sources, such as model inadequacy. Therefore, this thesis presents how unknown sources of uncertainty can be accounted for in calibration using Markov Chain Monte Carlo (MCMC) by assuming a variability in the calibration parameters. Two methods are presented, and both are based on MCMC, requiring numerous samples to converge. Hence, Gaussian Process (GP) surrogate modeling is used in place of the code to provide the calibration methods with inexpensive estimates of code responses. The first method is derivative-based, relying on differentiated GPs. While it effectively calibrates cladding oxidation, it struggles with fission gas release. Consequently, a more accurate method based on MH-within-Gibbs sampling is presented that successfully calibrates fission gas release.

The presented calibration methods use GP surrogate models to efficiently calibrate model parameters with inexpensive scalar estimates of code predictions. Beyond calibration, surrogate modeling is also essential when time-dependent predictions are needed for numerous fuel rod irradiations simultaneously. For example, if time-dependent predictions of fuel rod behavior are required in core optimization or core monitoring, calculation time can become a limiting factor. Therefore, this work also investigates neural network architectures for temporal data based on Temporal Convolutional Neural Networks (TCNs) and Fourier Neural Operators (FNOs) designed to model numerous fuel rods with varying irradiation histories. These networks accurately predict the behavior of thousands of fuel rods within seconds, significantly improving computational efficiency.