| A comprehensive survey of inverse uncertainty quantification of physical model parameters in nuclear system thermal–hydraulics codes X Wu, Z Xie, F Alsafadi, T Kozlowski Nuclear Engineering and Design 384, 111460, 2021 | 39 | 2021 |
| Towards improving the predictive capability of computer simulations by integrating inverse Uncertainty Quantification and quantitative validation with Bayesian hypothesis testing Z Xie, F Alsafadi, X Wu Nuclear Engineering and Design 383, 111423, 2021 | 12 | 2021 |
| Deep generative modeling-based data augmentation with demonstration using the BFBT benchmark void fraction datasets F Alsafadi, X Wu Nuclear Engineering and Design 415, 112712, 2023 | 3 | 2023 |
| Effect of mesh refinement on the solution of the inverse uncertainty quantification problem for transient physics RAA Saleem, FR Alsafadi, N Al-Abidah Progress in Nuclear Energy 152, 104360, 2022 | 1 | 2022 |
| ARTISANS—Artificial Intelligence for Simulation of Advanced Nuclear Systems for Nuclear Fission Technology A Akins, A Furlong, L Kohler, J Clifford, C Brady, F Alsafadi, X Wu Nuclear Engineering and Design 423, 113170, 2024 | | 2024 |
| Data-Driven Prediction and Uncertainty Quantification of PWR Crud-Induced Power Shift Using Convolutional Neural Networks A Furlong, F Alsafadi, S Palmtag, A Godfrey, X Wu arXiv preprint arXiv:2407.04726, 2024 | | 2024 |
| EFFECT OF PARAMETRIC TUNING ON THE SOLUTION OF THE INVERSE UNCERTAINTY QUANTIFICATION PROBLEM FR AL-SAFADI Jordan University of Science and Technology, 2020 | | 2020 |
