Xu Wu

Assistant Professor of Nuclear Engineering

  • 919-515-6570
  • Burlington Laboratory 2110
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Dr. Wu received his B.S. degree in nuclear engineering from the Shanghai Jiao Tong University in China in 2011. He obtained his Ph.D. degree in nuclear engineering in 2017 from the University of Illinois at Urbana – Champaign. Prior to joining NC State, he was a Postdoctoral Research Associate in the Department of Nuclear Science and Engineering at Massachusetts Institute of Technology. Dr. Wu’s research has revolved around uncertainty/sensitivity analysis, calibration, validation and reduced order modeling. The goal is to build a comprehensive framework that integrates calibration and validation to improve prediction. This framework accounts for all major sources of quantifiable uncertainties in Modeling & Simulation (M&S), i.e., uncertainties from parameter, experiment, model and code. It also builds an intelligent feedback between simulations and experiments and essentially bridges the gap between models and data. This framework provides a potential solution to the ANS Nuclear Grand Challenge on “Simulation & Experimentation”.

Dr. Wu’s current and future research aims at combining scientific Machine Learning, experimentation and M&S into a unified approach to improve the predictive capabilities of computer models and enable reduced reliance on expensive measurement data. Additionally, the application of such research will be focused on risk and economics evaluations of advanced nuclear reactors, such as small modular reactors and micro-reactors. The ultimate goal is to dramatically reduce the capital and operating costs of nuclear systems to maintain global technology leadership for nuclear energy.


B.S. 2011

Nuclear Engineering

Shanghai Jiao Tong University

M.S. 2013

Nuclear, Plasma and Radiological Engineering

University of Illinois at Urbana - Champaign

Ph.D. 2017

Nuclear, Plasma and Radiological Engineering

University of Illinois at Urbana - Champaign

Research Description

Dr. Wu's research interests include: (1) Calibration, Validation, Data Assimilation, Uncertainty and Sensitivity Analysis; (2) Computational Statistics, Reduced Order Modeling; (3) Bayesian Inference and Model Inversion; (4) Scientific Machine Learning; (5) System Thermal-Hydraulics, Nuclear Fuel Performance modeling, Multi-physics coupled simulation; (6) Small Modular Reactors, Micro-reactors


Bayesian inverse uncertainty quantification of a MOOSE-based melt pool model for additive manufacturing using experimental data
Xie, Z., Jiang, W., Wang, C., & Wu, X. (2022), ANNALS OF NUCLEAR ENERGY, 1. https://doi.org/10.1016/j.anucene.2021.108782
A comprehensive survey of inverse uncertainty quantification of physical model parameters in nuclear system thermal-hydraulics codes
Wu, X., Xie, Z., Alsafadi, F., & Kozlowski, T. (2021), NUCLEAR ENGINEERING AND DESIGN, 12. https://doi.org/10.1016/j.nucengdes.2021.111460
Application of Kriging and Variational Bayesian Monte Carlo method for improved prediction of doped UO2 fission gas release
Che, Y., Wu, X., Pastore, G., Li, W., & Shirvan, K. (2021), ANNALS OF NUCLEAR ENERGY, 153. https://doi.org/10.1016/j.anucene.2020.108046
Enhancing the One-Dimensional SFR Thermal Stratification Model via Advanced Inverse Uncertainty Quantification Methods
Lu, C., Wu, Z., & Wu, X. (2021), Nuclear Technology, 10, 1–19. https://doi.org/10.1080/00295450.2020.1805259
Towards improving the predictive capability of computer simulations by integrating inverse Uncertainty Quantification and quantitative validation with Bayesian hypothesis testing
Xie, Z., Alsafadi, F., & Wu, X. (2021), NUCLEAR ENGINEERING AND DESIGN, 11. https://doi.org/10.1016/j.nucengdes.2021.111423
System code evaluation of near-term accident tolerant claddings during pressurized water reactor station blackout accidents
Jin, Y., Wu, X., & Shirvan, K. (2020), NUCLEAR ENGINEERING AND DESIGN, 368. https://doi.org/10.1016/j.nucengdes.2020.110814
Demonstration of the relationship between sensitivity and identifiability for inverse uncertainty quantification
Wu, X., Shirvan, K., & Kozlowski, T. (2019), Journal of Computational Physics, 396, 12–30. https://doi.org/10.1016/j.jcp.2019.06.032
Gaussian Process–Based Inverse Uncertainty Quantification for TRACE Physical Model Parameters Using Steady-State PSBT Benchmark
Wang, C., Wu, X., & Kozlowski, T. (2019), Nuclear Science and Engineering, 193(1-2), 100–114. https://doi.org/10.1080/00295639.2018.1499279
Bayesian calibration and uncertainty quantification for trace based on PSBT benchmark
Wang, C., Wu, X., Borowiec, K., & Kozlowski, T. (2018), Transactions of the American Nuclear Society, 118, 419–422.
Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian Process, Part 2: Application to TRACE
Wu, X., Kozlowski, T., Meidani, H., & Shirvan, K. (2018), Nuclear Engineering and Design, 335, 417–431. https://doi.org/10.1016/j.nucengdes.2018.06.003

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Probabilistic and AI/ML Approaches in Structural Engineering, CNEFS Core Project
NCSU Center for Nuclear Energy Facilities and Structures (CNEFS)(1/01/22 - 12/31/22)
COBRA-TF (CTF) Modeling Applicable to High Burnup High Enrichment (HBHE) Fuel
US Dept. of Energy (DOE)(3/22/22 - 3/21/23)
Artificial Intelligence Based Process Control and Optimization for Advanced Manufacturing
US Dept. of Energy (DOE)(11/08/21 - 9/30/22)
Development of PINN for AM optimization
US Dept. of Energy (DOE)(1/06/20 - 9/30/20)