Ziyu Xie

Xie, Z., Yaseen, M., & Wu, X. (2024). Functional PCA and deep neural networks-based Bayesian inverse uncertainty quantification with transient experimental data. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 420. https://doi.org/10.1016/j.cma.2023.116721

Liu, Y., Mui, T., Xie, Z., & Hu, R. (2023). Benchmarking FFTF LOFWOS Test# 13 using SAM code: Baseline model development and uncertainty quantification. ANNALS OF NUCLEAR ENERGY, 192. https://doi.org/10.1016/j.anucene.2023.110010

Wang, C., Wu, X., Xie, Z., & Kozlowski, T. (2023). Scalable Inverse Uncertainty Quantification by Hierarchical Bayesian Modeling and Variational Inference. ENERGIES, 16(22). https://doi.org/10.3390/en16227664

Xie, Z., Jiang, W., Wang, C., & Wu, X. (2022). Bayesian inverse uncertainty quantification of a MOOSE-based melt pool model for additive manufacturing using experimental data. ANNALS OF NUCLEAR ENERGY, 165. https://doi.org/10.1016/j.anucene.2021.108782

Wu, X., Xie, Z., Alsafadi, F., & Kozlowski, T. (2021). A comprehensive survey of inverse uncertainty quantification of physical model parameters in nuclear system thermal-hydraulics codes. NUCLEAR ENGINEERING AND DESIGN, 384. https://doi.org/10.1016/j.nucengdes.2021.111460

Xie, Z., Alsafadi, F., & Wu, X. (2021). Towards improving the predictive capability of computer simulations by integrating inverse Uncertainty Quantification and quantitative validation with Bayesian hypothesis testing. NUCLEAR ENGINEERING AND DESIGN, 383. https://doi.org/10.1016/j.nucengdes.2021.111423