Applying Gaussian Process Regression for Machine Learning-Assisted Reactor Simulations

This study explores the integration of machine learning, specifically Gaussian Process Regression (GPR), into traditional reactor core simulations. Building upon previous work on Boiling Water Reactors (BWR), GPR is implemented to predict and correct errors in lower-fidelity simulation outcomes. The findings demonstrate significant improvements in prediction accuracy when GPR is coupled with the diffusion-based core simulator, exhibiting remarkable reductions in both keff and nodal power errors. The comparison reveals that the GPR-enhanced core simulation model significantly outperforms both the standalone simulation and a combination of simulation with Multivariate Linear Regression. It also competes effectively with the performance of a Deep Neural Network-enhanced model. Importantly, this methodology enhances simulation accuracy while maintaining low computational costs. The research emphasizes the vast potential of machine learning, particularly GPR, in progressing nuclear reactor simulations, highlighting the immense value of combining traditional simulation methods with advanced statistical learning techniques.