Prediction and uncertainty quantification of SAFARI-1 axial neutron flux profiles with neural networks
Abstract In this study, Deep Neural Networks (DNNs) are used to predict the assembly axial
neutron flux profiles in the SAFARI-1 research reactor, with quantified uncertainties in the …
neutron flux profiles in the SAFARI-1 research reactor, with quantified uncertainties in the …
Preliminary development of machine learning-based error correction model for low-fidelity reactor physics simulation
MR Oktavian, J Nistor, JT Gruenwald, Y Xu - Annals of Nuclear Energy, 2023 - Elsevier
Better prediction capability in reactor simulation procedures can result in better fuel
planning, increased safety, and compliance with the Technical Specifications. Motivated by …
planning, increased safety, and compliance with the Technical Specifications. Motivated by …
Quantification of deep neural network prediction uncertainties for VVUQ of machine learning models
Recent performance breakthroughs in artificial intelligence (AI) and machine learning (ML),
especially advances in deep learning, the availability of powerful and easy-to-use ML …
especially advances in deep learning, the availability of powerful and easy-to-use ML …
Clustering and uncertainty analysis to improve the machine learning-based predictions of SAFARI-1 control follower assembly axial neutron flux profiles
The goal of this work is to develop accurate Machine Learning (ML) models for predicting
the assembly axial neutron flux profiles in the SAFARI-1 research reactor, trained by …
the assembly axial neutron flux profiles in the SAFARI-1 research reactor, trained by …