Xu Wu

Akins, A., Furlong, A., Kohler, L., Clifford, J., Brady, C., Alsafadi, F., & Wu, X. (2024). ARTISANS—Artificial Intelligence for Simulation of Advanced Nuclear Systems for Nuclear Fission Technology. Nuclear Engineering and Design. https://doi.org/10.1016/j.nucengdes.2024.113170

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

Alsafadi, F., & Wu, X. (2023). Deep generative modeling-based data augmentation with demonstration using the BFBT benchmark void fraction datasets. NUCLEAR ENGINEERING AND DESIGN, 415. https://doi.org/10.1016/j.nucengdes.2023.112712

Yaseen, M., Yushu, D., German, P., & Wu, X. (2023). Fast and accurate reduced-order modeling of a MOOSE-based additive manufacturing model with operator learning. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 129(7-8), 3123–3139. https://doi.org/10.1007/s00170-023-12471-1

Wang, C., Wu, X., & Kozlowski, T. (2023). Inverse Uncertainty Quantification by Hierarchical Bayesian Modeling and Application in Nuclear System Thermal-Hydraulics Codes (ArXiv Preprint No. 2305.16622). https://doi.org/10.48550/arXiv.2305.16622

Moloko, L. E., Bokov, P. M., Wu, X., & Ivanov, K. N. (2023). Prediction and uncertainty quantification of SAFARI-1 axial neutron flux profiles with neural networks. ANNALS OF NUCLEAR ENERGY, 188. https://doi.org/10.1016/j.anucene.2023.109813

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

Yaseen, M., & Wu, X. (2022, November 4). Quantification of Deep Neural Network Prediction Uncertainties for VVUQ of Machine Learning Models. NUCLEAR SCIENCE AND ENGINEERING, Vol. 11. https://doi.org/10.1080/00295639.2022.2123203

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

Che, Y., Wu, X., Pastore, G., Li, W., & Shirvan, K. (2021). Application of Kriging and Variational Bayesian Monte Carlo method for improved prediction of doped UO2 fission gas release. ANNALS OF NUCLEAR ENERGY, 153. https://doi.org/10.1016/j.anucene.2020.108046

Lu, C., Wu, Z., & Wu, X. (2021). Enhancing the One-Dimensional SFR Thermal Stratification Model via Advanced Inverse Uncertainty Quantification Methods. Nuclear Technology, 10, 1–19. https://doi.org/10.1080/00295450.2020.1805259

Yan, E., Sandhu, H., Bodda, S., Gupta, A., Wu, X., & Sabharwall, P. (2021). Structural Health Monitoring of Microreactor Safety Systems Using Convolutional Neural Networks. https://doi.org/10.2172/1824205

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

Jin, Y., Wu, X., & Shirvan, K. (2020). System code evaluation of near-term accident tolerant claddings during pressurized water reactor station blackout accidents. NUCLEAR ENGINEERING AND DESIGN, 368. https://doi.org/10.1016/j.nucengdes.2020.110814

Wu, X., Shirvan, K., & Kozlowski, T. (2019). Demonstration of the relationship between sensitivity and identifiability for inverse uncertainty quantification. Journal of Computational Physics, 396, 12–30. https://doi.org/10.1016/j.jcp.2019.06.032

Wang, C., Wu, X., & Kozlowski, T. (2019). Gaussian Process–Based Inverse Uncertainty Quantification for TRACE Physical Model Parameters Using Steady-State PSBT Benchmark. Nuclear Science and Engineering, 193(1-2), 100–114. https://doi.org/10.1080/00295639.2018.1499279

Wang, C., Wu, X., Borowiec, K., & Kozlowski, T. (2018). Bayesian calibration and uncertainty quantification for trace based on PSBT benchmark. Transactions of the American Nuclear Society, 118, 419–422. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-85062963843&partnerID=MN8TOARS

Wu, X., Kozlowski, T., Meidani, H., & Shirvan, K. (2018). Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian Process, Part 2: Application to TRACE. Nuclear Engineering and Design, 335, 417–431. https://doi.org/10.1016/j.nucengdes.2018.06.003

Wu, X., Kozlowski, T., Meidani, H., & Shirvan, K. (2018). Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian process, Part 1: Theory. Nuclear Engineering and Design, 335, 339–355. https://doi.org/10.1016/j.nucengdes.2018.06.004

Wu, X., Kozlowski, T., & Meidani, H. (2018). Kriging-based inverse uncertainty quantification of nuclear fuel performance code BISON fission gas release model using time series measurement data. Reliability Engineering and System Safety, 169, 422–436. https://doi.org/10.1016/j.ress.2017.09.029

Wu, X., Shirvan, K., & Kozlowski, T. (2018). On the connection between sensitivity and identifiability for inverse uncertainty quantification. Transactions of the American Nuclear Society, 118, 411–414. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-85062995468&partnerID=MN8TOARS

Che, Y., Wu, X., Pastore, G., Hales, J., & Shirvan, K. (2018). Sensitivity and uncertainty analysis for fuel performance evaluation of Cr <inf>2</inf> O <inf>3</inf> -doped UO <inf>2</inf> fuel under LB-LOCA. Transactions of the American Nuclear Society, 119, 440–443. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-85060860913&partnerID=MN8TOARS

Wu, X., & Shirvan, K. (2018). System code evaluation of accident tolerant claddings during BWR station blackout accident. Transactions of the American Nuclear Society, 119, 444–447. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-85060862292&partnerID=MN8TOARS

Wu, X., Shirvan, K., & Kozlowski, T. (2018). Validating trace void fraction predictive capability using the quantitative area validation metric. Transactions of the American Nuclear Society, 118, 423–426. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-85062957450&partnerID=MN8TOARS

Wu, X., Mui, T., Hu, G., Meidani, H., & Kozlowski, T. (2017). Inverse uncertainty quantification of TRACE physical model parameters using sparse gird stochastic collocation surrogate model. Nuclear Engineering and Design, 319, 185–200. https://doi.org/10.1016/j.nucengdes.2017.05.011

Wu, X., & Kozlowski, T. (2017). Inverse uncertainty quantification of reactor simulations under the Bayesian framework using surrogate models constructed by polynomial chaos expansion. Nuclear Engineering and Design, 313, 29–52. https://doi.org/10.1016/j.nucengdes.2016.11.032

Wu, X., & Kozlowski, T. (2017). Kriging-based inverse uncertainty quantification of BISON fission gas release model. Transactions of the American Nuclear Society, 116, 629–632. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-85033468956&partnerID=MN8TOARS

Wang, C., Wu, X., & Kozlowski, T. (2017). Sensitivity and uncertainty analysis of TRACE Physical Model Parameters based on PSBT benchmark using Gaussian process emulator. 17th International Topical Meeting on Nuclear Reactor Thermal Hydraulics, NURETH 2017, 2017-September. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-85051935457&partnerID=MN8TOARS

Wang, C., Wu, X., & Kozlowski, T. (2017). Surrogate-based inverse uncertainty quantification of TRACE physical model parameters using steady-state PSBT void fraction data. 17th International Topical Meeting on Nuclear Reactor Thermal Hydraulics, NURETH 2017, 2017-September. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-85051987392&partnerID=MN8TOARS

Wu, X., & Kozlowski, T. (2015). Coupling of system thermal-hydraulics and Monte-Carlo code: Convergence criteria and quantification of correlation between statistical uncertainty and coupled error. Annals of Nuclear Energy, 75, 377–387. https://doi.org/10.1016/j.anucene.2014.08.016

Rose, M., Downar, T. J., Wu, X., & Kozlowski, T. (2015). Evaluation of accident tolerant FeCrAl coating for PWR cladding under normal operating conditions with coupled neutron transport and fuel performance. Mathematics and Computations, Supercomputing in Nuclear Applications and Monte Carlo International Conference, M and C+SNA+MC 2015, 3, 2334–2344. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-84949522107&partnerID=MN8TOARS

Wu, X., Kozlowski, T., & Hales, J. D. (2015). Neutronics and fuel performance evaluation of accident tolerant FeCrAl cladding under normal operation conditions. Annals of Nuclear Energy, 85, 763–775. https://doi.org/10.1016/j.anucene.2015.06.032

Wu, X., & Kozlowski, T. (2014). Coupling of system thermal-hydraulics and monte-carlo method for a consistent thermal-hydraulics-reactor physics feedback. International Congress on Advances in Nuclear Power Plants, ICAPP 2014, 2, 1164–1174. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-84907077778&partnerID=MN8TOARS

Wu, X., Kozlowski, T., & Heuser, B. J. (2014). Neutronics analysis of improved accident tolerance LWR fuel by modifing Zircaloy cladding of fuel pins. International Congress on Advances in Nuclear Power Plants, ICAPP 2014, 1, 159–166. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-84907085456&partnerID=MN8TOARS

Wu, X., Sabharwall, P., & Hales, J. (2014). Neutronics and Fuel Performance Evaluation of Accident Tolerant Fuel under Normal Operation Conditions. https://doi.org/10.2172/1166052

Wu, X., & Kozlowski, T. (2014). Uncertainty quantification for coupled Monte Carlo and thermal-hydraulics codes. Transactions of the American Nuclear Society, 110, 189–191. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-84904692027&partnerID=MN8TOARS

Heuser, B. J., Kozlowski, T., & Xu, W. (2013). Engineered Zircaloy cladding modifications for improved accident tolerance of LWR fuel: A summary. LWR Fuel Performance Meeting, Top Fuel 2013, 1, 56–58. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-84902344678&partnerID=MN8TOARS