| Quantification of deep neural network prediction uncertainties for VVUQ of machine learning models M Yaseen, X Wu Nuclear Science and Engineering 197 (5), 947-966, 2023 | 12 | 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 | 7 | 2024 |
| Fast and accurate reduced-order modeling of a MOOSE-based additive manufacturing model with operator learning M Yaseen, D Yushu, P German, X Wu The International Journal of Advanced Manufacturing Technology 129 (7), 3123 …, 2023 | 4 | 2023 |
| 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 |
| Reduced order modeling of a MOOSE-based advanced manufacturing model with operator learning M Yaseen, D Yushu, P German, X Wu arXiv preprint arXiv:2308.09691, 2023 | 1 | 2023 |
| An Investigation on Machine Learning Predictive Accuracy Improvement and Uncertainty Reduction using VAE-based Data Augmentation F Alsafadi, M Yaseen, X Wu arXiv preprint arXiv:2410.19063, 2024 | | 2024 |
| Uncertainty Quantification in Deep Neural Network Models for Nuclear Reactor Benchmarks. MQ Yaseen | | 2024 |
| Sensitivity and Uncertainty Analysis in Pebble-Bed Reactors: A Study Using the High-Temperature Code Package (Hcp) M Yaseen, A Sadek, W Osman, MR Altahhan, M Avramova, K Ivanov Maria and Ivanov, Kostadin, Sensitivity and Uncertainty Analysis in Pebble …, 0 | | |
Xu WuAssistant Professor of Nuclear Engineering, North Carolina State University在 ncsu.edu 的电子邮件经过验证
