Xu Wu
Assistant Professor of Nuclear Engineering
- 919-515-6570
- xwu27@ncsu.edu
- 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 NCSU, he was a Postdoctoral Research Associate in the Department of Nuclear Science and Engineering at Massachusetts Institute of Technology. Dr. Wu is the principal investigator (PI) of the ARTISANS (ARTificial Intelligence for Simulation of Advanced Nuclear Systems) research group at NCSU-NE. The research in the ARTISANS group revolves around uncertainty quantification (UQ) and scientific machine learning (SciML). The goal is to combine SciML, experimentation and modeling & simulation (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.
Education
Nuclear Engineering
Shanghai Jiao Tong University
Nuclear, Plasma and Radiological Engineering
University of Illinois at Urbana - Champaign
Nuclear, Plasma and Radiological Engineering
University of Illinois at Urbana - Champaign
Nuclear Science and Engineering
Massachusetts Institute of Technology
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 inverse problems; (4) scientific machine learning and deep generative learning; (5) system thermal-hydraulics, nuclear fuel performance modeling, multi-physics coupled simulation; (6) advanced reactors, small modular reactors, micro-reactors.
Publications
- Functional PCA and deep neural networks-based Bayesian inverse uncertainty quantification with transient experimental data
- Xie, Z., Yaseen, M., & Wu, X. (2024), COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 420. https://doi.org/10.1016/j.cma.2023.116721
- Deep generative modeling-based data augmentation with demonstration using the BFBT benchmark void fraction datasets
- Alsafadi, F., & Wu, X. (2023), NUCLEAR ENGINEERING AND DESIGN, 415. https://doi.org/10.1016/j.nucengdes.2023.112712
- Inverse Uncertainty Quantification by Hierarchical Bayesian Modeling and Application in Nuclear System Thermal-Hydraulics Codes
- Wang, C., Wu, X., & Kozlowski, T. (2023). , (ArXiv Preprint No. 2305.16622). https://doi.org/10.48550/arXiv.2305.16622
- Prediction and uncertainty quantification of SAFARI-1 axial neutron flux profiles with neural networks
- Moloko, L. E., Bokov, P. M., Wu, X., & Ivanov, K. N. (2023), ANNALS OF NUCLEAR ENERGY, 188. https://doi.org/10.1016/j.anucene.2023.109813
- Scalable Inverse Uncertainty Quantification by Hierarchical Bayesian Modeling and Variational Inference
- Wang, C., Wu, X., Xie, Z., & Kozlowski, T. (2023), ENERGIES, 16(22). https://doi.org/10.3390/en16227664
- 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, 165. https://doi.org/10.1016/j.anucene.2021.108782
- Quantification of Deep Neural Network Prediction Uncertainties for VVUQ of Machine Learning Models
- Yaseen, M., & Wu, X. (2022, November 4), NUCLEAR SCIENCE AND ENGINEERING, Vol. 11. https://doi.org/10.1080/00295639.2022.2123203
- 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, 384. 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
Grants
- Using Machine Learning to Predict Locations with Crud Buildup
- Consortium for Nuclear Power (CNP)(7/01/22 - 6/30/23)
- Statistical Approaches to Reduce Uncertainty in PSHA, CNEFS Enhancement Project
- Electricite de France (EDF/DER)(1/01/23 - 12/31/23)
- Probabilistic and AI/ML Approaches in Structural Engineering, CNEFS Core Project
- NCSU Center for Nuclear Energy Facilities and Structures (CNEFS)(1/01/22 - 12/31/23)
- COBRA-TF (CTF) Modeling Applicable to High Burnup High Enrichment (HBHE) Fuel
- US Dept. of Energy (DOE)(3/22/22 - 8/31/23)
- Artificial Intelligence Based Process Control and Optimization for Advanced Manufacturing
- US Dept. of Energy (DOE)(11/08/21 - 9/30/23)
- Development of PINN for AM optimization
- US Dept. of Energy (DOE)(1/06/20 - 9/30/20)