Aidan John Furlong

Data-driven prediction and uncertainty quantification of PWR crud-induced power shift using convolutional neural networks

ENERGY, 316.

ARTISANS-Artificial Intelligence for Simulation of Advanced Nuclear Systems for Nuclear Fission Technology

NUCLEAR ENGINEERING AND DESIGN, 423.

Analysis of the LatticeNet neural network framework’s performance using prediction-calculated temperature coefficients in PWR assemblies

Annals of Nuclear Energy.

Investigation of Monte Carlo trained CNNs for neutronics predictions of typical and atypical PWR assemblies

Progress in Nuclear Energy, 166.

Machine Learning-based Prediction of Crud Buildup Locations in Pressurized Water Reactors

Transactions of the American Nuclear Society, 129, 456–459.

Predicting PWR Fuel Assembly CIPS Susceptibility with Convolutional Neural Networks: Performance and Uncertainty Quantification

International Conference on Physics of Reactors (PHYSOR 2024).

Prediction of CIPS Susceptibility in PWR Assemblies Using 3D Convolutional Neural Networks

https://www.lib.ncsu.edu/resolver/1840.20/41647

Using Neural Networks to Predict Pin Powers in Reflective PWR Fuel Assemblies with Varying Pin Size

International Conference on Physics of Reactors 2022, 15–20,