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 | 33 | 2021 |
Bayesian inverse uncertainty quantification of a MOOSE-based melt pool model for additive manufacturing using experimental data Z Xie, W Jiang, C Wang, X Wu Annals of Nuclear Energy 165, 108782, 2022 | 12 | 2022 |
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 |
Scalable inverse uncertainty quantification by hierarchical bayesian modeling and variational inference C Wang, X Wu, Z Xie, T Kozlowski Energies 16 (22), 7664, 2023 | 5 | 2023 |
Functional PCA and deep neural networks-based Bayesian inverse uncertainty quantification with transient experimental data Z Xie, M Yaseen, X Wu Computer Methods in Applied Mechanics and Engineering 420, 116721, 2024 | 1 | 2024 |
Uncertainty Quantification of Deep Neural Network Predictions for Time-dependent Responses with Functional PCA M Yaseen, Z Xie, X Wu In Proceedings of the 20th International Topical Meeting on Nuclear Reactor …, 2023 | 1 | 2023 |
A systematic approach for the adequacy analysis of a set of experimental databases: Application in the framework of the ATRIUM activity J Baccou, T Glantz, A Ghione, L Sargentini, P Fillion, G Damblin, R Sueur, ... Nuclear Engineering and Design 421, 113035, 2024 | | 2024 |
Benchmarking FFTF LOFWOS Test# 13 using SAM code: Baseline model development and uncertainty quantification Y Liu, T Mui, Z Xie, R Hu Annals of Nuclear Energy 192, 110010, 2023 | | 2023 |
Inverse uncertainty quantification of a MOOSE-based melt pool model for additive manufacturing Z Xie, X Wu, W Jiang, C Wang Proceedings of the international conference on mathematics and computational …, 2021 | | 2021 |