13 citations · 15 across the 5 of their papers we have counts for
5 papers
Uncertainty Quantification for Data-Driven Machine Learning Models in Nuclear Engineering Applications: Where We Are and What Do We Need?
Xu Wu, Lesego E. Moloko, Pavel M. Bokov +3
Machine learning (ML) has been leveraged to tackle a diverse range of tasks in almost all branches of nuclear engineering. Many of the successes in ML applications can be attribute…
Clustering and Uncertainty Analysis to Improve the Machine Learning-based Predictions of SAFARI-1 Control Follower Assembly Axial Neutron Flux Profiles
Lesego Moloko, Pavel Bokov, Xu Wu +1
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 measu…
Prediction and Uncertainty Quantification of SAFARI-1 Axial Neutron Flux Profiles with Neural Networks
Lesego E. Moloko, Pavel M. Bokov, Xu Wu +1
Artificial Neural Networks (ANNs) have been successfully used in various nuclear engineering applications, such as predicting reactor physics parameters within reasonable time and…
Neutronic Characterization of the Megapie Target
Stefano Panebianco, Olivier Bringer, Pavel Bokov +10
The MEGAPIE project is one of the key experiments towards the feasibility of Accelerator Driven Systems. On-line operation and post-irradiation analysis will provide the scientific…
Delayed neutrons measurement at the MEGAPIE target
Stefano Panebianco, Pavel Bokov, Diane Dore +4
In the framework of the Neutronic and Nuclear Assessment Task Group of the MEGAPIE experiment we measured the delayed neutron (DN) flux at the top of the target. The measurement wa…