Aidan John Furlong

A Three-Stage Bayesian Transfer Learning Framework to Improve Predictions in Data-Scarce Domains

Furlong, A., Salko, R., Zhao, X., & Wu, X. (2025, October 30). ArXiv (Cornell University).

Cycle Optimization for a 4-loop PWR Using Multicycle Constraint Annealing

Proceedings of Advances in Nuclear Fuel Management Anfm 2025, pp. 443–452.

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

Furlong, A., Alsafadi, F., Palmtag, S., Godfrey, A., & Wu, X. (2025, January 11). Energy, Vol. 316.

Deployment of Traditional and Hybrid Machine Learning for Critical Heat Flux Prediction in the CTF Thermal Hydraulics Code

Furlong, A., Zhao, X., Salko, R., & Wu, X. (2025, May 12). ArXiv (Cornell University).

Development and Deployment of Hybrid ML Models for Critical Heat Flux Prediction in Annulus Geometries

Arxiv.

Native Fortran Implementation of TensorFlow-Trained Deep and Bayesian Neural Networks

Transactions of the American Nuclear Society, Vol. 132, pp. 474–477.

Physics-based hybrid machine learning for critical heat flux prediction with uncertainty quantification

Furlong, A., Zhao, X., Salko, R. K., & Wu, X. (2025, July 15). Applied Thermal Engineering, Vol. 279.

Predicting critical heat flux with uncertainty quantification and domain generalization using conditional variational autoencoders and deep neural networks

Alsafadi, F., Furlong, A., & Wu, X. (2025, April 28). Annals of Nuclear Energy, Vol. 220.

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

Akins, A., Furlong, A., Kohler, L., Clifford, J., Brady, C., Alsafadi, F., & Wu, X. (2024, April 1). Nuclear Engineering and Design, Vol. 423.

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

Furlong, A., & Watson, J. (2024, March 30). Annals of Nuclear Energy, Vol. 203.

Comparison of Hybrid and Pure Machine Learning Models in Limited Data Scenarios

Transactions of the American Nuclear Society, Vol. 131, pp. 494–497.

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

Furlong, A., Alsafadi, F., Palmtag, S., Godfrey, A., Hayes, S., & Wu, X. (2024, January 1). Proceedings of the International Conference on Physics of Reactors Physor 2024, pp. 1684–1693.

Prediction and Uncertainty Quantification of Critical Heat Flux - A Comparison Between Generative Conditional VAEs and DNNs

Proceedings of Advances in Thermal Hydraulics Ath 2024, pp. 332–341.

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

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

The Use of Transfer Learning to Extend Critical Heat Flux Predictions

Proceedings of Advances in Thermal Hydraulics Ath 2024, pp. 366–375.

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

Furlong, A., Watson, J., & Shriver, F. (2023, November 16). Progress in Nuclear Energy, Vol. 166.

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

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

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

Furlong, A., Shriver, F., & Watson, J. (2022, January 1). Proceedings of the International Conference on Physics of Reactors Physor 2022, pp. 2706–2715.