Education

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

Publications

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

Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian process, Part 1: Theory

Wu, X., Kozlowski, T., Meidani, H., & Shirvan, K. (2018), Nuclear Engineering and Design, 335, 339–355. https://doi.org/10.1016/j.nucengdes.2018.06.004

Kriging-based inverse uncertainty quantification of nuclear fuel performance code BISON fission gas release model using time series measurement data

Wu, X., Kozlowski, T., & Meidani, H. (2018), Reliability Engineering and System Safety, 169, 422–436. https://doi.org/10.1016/j.ress.2017.09.029

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Grants

Development of PINN for AM optimization

US Dept. of Energy (DOE)(1/06/20 - 9/30/20)

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Development of PINN for AM optimization

Sponsor: US Dept. of Energy (DOE)

Start Date: 1/06/20

End Date: 9/30/20

Abstract

The major objective is to develop Physics-Informed Neural Networks (PINN) techniques to assist the optimization of Additive Manufacturing (AM) process parameters such as applied laser power, traveling speed and scan style, to achieve desired nuclear fuel thermal-mechanical properties and fracture toughness at engineering scale. The project will focus on two tasks: (1) developing and demonstrating efficient Physics-Informed Deep Learning (PIDL) algorithm to solve the underlying Partial Differential Equations (PDEs) in the AM process; (2) determining an optimal set of AM process parameters through optimization using the PINN model.

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