Dr. Xu Wu is an Assistant Professor at the Department of Nuclear Engineering of the North Carolina State University. his research interests include: (1) Calibration, Validation, Data Assimilation, Uncertainty and Sensitivity Analysis; (2) Computational Statistics, Reduced Order Modeling, Bayesian Inference and Model Inversion; (3) mathematical representations of model discrepancy to improve model predictive capabilities; (4) Physics-Informed Machine Learning, Deep Learning; (5) System Thermal-Hydraulics, Nuclear Fuel Performance Modeling, Multi-physics Coupled Simulation; (6) Accident Tolerant Fuels (ATFs) modeling, etc.

Works (38)

Updated: April 15th, 2024 05:33

2024 journal article

ARTISANS—Artificial Intelligence for Simulation of Advanced Nuclear Systems for Nuclear Fission Technology

Nuclear Engineering and Design.

By: A. Akins, A. Furlong, L. Kohler, J. Clifford, C. Brady, F. Alsafadi, X. Wu*

Source: ORCID
Added: April 8, 2024

2024 journal article

Functional PCA and deep neural networks-based Bayesian inverse uncertainty quantification with transient experimental data

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 420.

By: Z. Xie n, M. Yaseen n & X. Wu n

author keywords: Bayesian inverse UQ; Functional alignment; Functional PCA; Neural networks
TL;DR: This work focuses on developing an inverse UQ process for time-dependent responses, using functional principal component analysis (PCA) and deep neural network (DNN)-based surrogate models, and shows an improvement in reducing the dimension of the TRACE transient simulations. (via Semantic Scholar)
Sources: ORCID, Web Of Science, NC State University Libraries
Added: January 25, 2024

2023 journal article

Deep generative modeling-based data augmentation with demonstration using the BFBT benchmark void fraction datasets

NUCLEAR ENGINEERING AND DESIGN, 415.

By: F. Alsafadi n & X. Wu n

author keywords: Deep generative modeling; Generative adversarial networks; Normalizing flows; Variational autoencoders
TL;DR: The findings shows that DGMs have a great potential to augment scientific data in nuclear engineering, which proves effective for expanding the training dataset and enabling other DL models to be trained more accurately. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries, ORCID
Added: December 18, 2023

2023 journal article

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.

By: M. Yaseen n, D. Yushu*, P. German* & X. Wu n

author keywords: Additive manufacturing; Reduced-order modeling; Fourier neural operator; Deep operator network; MOOSE framework
Sources: Web Of Science, NC State University Libraries
Added: April 8, 2024

2023 report

Inverse Uncertainty Quantification by Hierarchical Bayesian Modeling and Application in Nuclear System Thermal-Hydraulics Codes

(ArXiv Preprint No. 2305.16622).

By: C. Wang, X. Wu* & T. Kozlowski

Sources: NC State University Libraries, NC State University Libraries
Added: September 14, 2023

2023 journal article

Prediction and uncertainty quantification of SAFARI-1 axial neutron flux profiles with neural networks

ANNALS OF NUCLEAR ENERGY, 188.

By: L. Moloko n, P. Bokov n, X. Wu n & K. Ivanov n

author keywords: Uncertainty quantification; Deep neural networks; Bayesian Neural Networks; Monte Carlo dropout
TL;DR: Deep Neural Networks are used to predict the assembly axial neutron flux profiles in the SAFARI-1 research reactor with quantified uncertainties in the ANN predictions and extrapolation to cycles not used in the training process, indicating good prediction and generalization capability. (via Semantic Scholar)
Sources: ORCID, Web Of Science, NC State University Libraries
Added: March 23, 2023

2023 journal article

Scalable Inverse Uncertainty Quantification by Hierarchical Bayesian Modeling and Variational Inference

ENERGIES, 16(22).

By: C. Wang*, X. Wu n, Z. Xie n & T. Kozlowski*

author keywords: inverse uncertainty quantification; hierarchical Bayesian; variational inference; nuclear thermal-hydraulics
Sources: Web Of Science, NC State University Libraries
Added: January 2, 2024

2022 journal article

Bayesian inverse uncertainty quantification of a MOOSE-based melt pool model for additive manufacturing using experimental data

ANNALS OF NUCLEAR ENERGY, 165.

By: Z. Xie n, W. Jiang*, C. Wang* & X. Wu n

author keywords: Inverse uncertainty quantification; Melt pool; Additive manufacturing
Sources: Web Of Science, NC State University Libraries
Added: November 29, 2021

2022 article

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.

By: M. Yaseen n & X. Wu n

author keywords: Uncertainty quantification; Deep Neural Network; Monte Carlo Dropout; Deep Ensemble; Bayesian Neural Network
TL;DR: Three techniques for UQ of DNNs are compared, namely, Monte Carlo Dropout, Deep Ensembles (DE), and Bayesian Neural Networks (BNNs), and it is found that the three methods typically require different DNN architectures and hyperparameters to optimize their performance. (via Semantic Scholar)
Sources: ORCID, Web Of Science, NC State University Libraries
Added: November 3, 2022

2021 journal article

A comprehensive survey of inverse uncertainty quantification of physical model parameters in nuclear system thermal-hydraulics codes

NUCLEAR ENGINEERING AND DESIGN, 384.

By: X. Wu n, Z. Xie n, F. Alsafadi n & T. Kozlowski*

author keywords: Inverse uncertainty quantification; Calibration; Physical model parameters; Frequentist; Bayesian; Empirical
TL;DR: This review paper aims to provide a comprehensive and comparative discussion of the major aspects of the IUQ methodologies that have been used on the physical models in system thermal-hydraulics codes, including solidity, complexity, accessibility, independence, flexibility, comprehensiveness, transparency, and tractability. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: November 1, 2021

2021 journal article

Application of Kriging and Variational Bayesian Monte Carlo method for improved prediction of doped UO2 fission gas release

ANNALS OF NUCLEAR ENERGY, 153.

author keywords: Doped fuel; Variational Bayesian Monte Carlo (VBMC); Bayesian inference; Kriging; Principal Component Analysis (PCA)
TL;DR: A novel optimization framework, Variational Bayesian Monte Carlo (VBMC) is demonstrated as a low-cost nonintrusive approach for Bayesian calibration and compared to the conventional statistical Markov Chain Monte Carlo sampling showing similar accuracy but superior efficiency. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Sources: Web Of Science, NC State University Libraries
Added: February 15, 2021

2021 journal article

Enhancing the One-Dimensional SFR Thermal Stratification Model via Advanced Inverse Uncertainty Quantification Methods

Nuclear Technology, 10, 1–19.

By: C. Lu*, Z. Wu* & X. Wu n

author keywords: Thermal stratification; sodium-cooled fast reactor; sensitivity analysis; inverse uncertainty quantification
UN Sustainable Development Goal Categories
Source: ORCID
Added: October 17, 2020

2021 report

Structural Health Monitoring of Microreactor Safety Systems Using Convolutional Neural Networks

Sources: Crossref, NC State University Libraries
Added: June 24, 2023

2021 journal article

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.

By: Z. Xie n, F. Alsafadi n & X. Wu n

author keywords: Inverse Uncertainty Quantification; Quantitative validation; Bayesian hypothesis testing; Bayes factor; ANS nuclear grand challenge
TL;DR: This paper proposes a comprehensive framework to integrate results from inverse UQ and quantitative validation to provide robust predictions to account for all available sources of uncertainties in a rigorous Uncertainty Quantification process. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: October 12, 2021

2020 journal article

System code evaluation of near-term accident tolerant claddings during pressurized water reactor station blackout accidents

NUCLEAR ENGINEERING AND DESIGN, 368.

By: Y. Jin*, X. Wu n & K. Shirvan*

author keywords: Accident Tolerant Fuel; FeCrAl; Cr-coating; Station Blackout
UN Sustainable Development Goal Categories
Sources: Web Of Science, NC State University Libraries
Added: November 2, 2020

2019 journal article

Demonstration of the relationship between sensitivity and identifiability for inverse uncertainty quantification

Journal of Computational Physics, 396, 12–30.

By: X. Wu*, K. Shirvan* & T. Kozlowski*

author keywords: Inverse uncertainty quantification; Modular Bayesian approach; Identifiability; Sensitivity
TL;DR: It is shown that identifiability is largely related to the sensitivity of the calibration parameters with regards to the chosen responses, and an improved modular Bayesian approach for inverse UQ is adopted that does not require priors for the model discrepancy term. (via Semantic Scholar)
Source: ORCID
Added: July 5, 2019

2019 journal article

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.

By: C. Wang*, X. Wu* & T. Kozlowski*

Contributors: C. Wang*, X. Wu* & T. Kozlowski*

author keywords: Inverse uncertainty quantification; Gaussian process; physical model parameter uncertainty; PSBT benchmark
TL;DR: The issue of missing uncertainty information for physical model parameters in the thermal-hydraulic code TRACE is addressed with inverse uncertainty quantification (IUQ), using the steady-state void fraction experimental data in the Organisation for Economic Co-operation and Development/Nuclear Energy Agency PSBT (Pressurized water reactor Sub-channel and Bundle Tests benchmark). (via Semantic Scholar)
UN Sustainable Development Goal Categories
7. Affordable and Clean Energy (OpenAlex)
Source: ORCID
Added: July 5, 2019

2018 conference paper

Bayesian calibration and uncertainty quantification for trace based on PSBT benchmark

Transactions of the American Nuclear Society, 118, 419–422. http://www.scopus.com/inward/record.url?eid=2-s2.0-85062963843&partnerID=MN8TOARS

By: C. Wang, X. Wu, K. Borowiec & T. Kozlowski

Contributors: C. Wang, X. Wu, K. Borowiec & T. Kozlowski

Source: ORCID
Added: July 5, 2019

2018 journal article

Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian Process, Part 2: Application to TRACE

Nuclear Engineering and Design, 335, 417–431.

Contributors: X. Wu*, T. Kozlowski*, H. Meidani* & K. Shirvan*

author keywords: Inverse uncertainty quantification; Bayesian calibration; Gaussian Process; Modular Bayesian; Model discrepancy
TL;DR: This research addresses the “lack of input uncertainty information” issue for TRACE physical input parameters, which was usually ignored or described using expert opinion or user self-assessment in previous work. (via Semantic Scholar)
Source: ORCID
Added: July 5, 2019

2018 journal article

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

Nuclear Engineering and Design, 335, 339–355.

Contributors: X. Wu*, T. Kozlowski*, H. Meidani* & K. Shirvan*

author keywords: Inverse uncertainty quantification; Bayesian calibration; Gaussian process; Modular Bayesian; Model discrepancy
TL;DR: This paper used Bayesian analysis to establish the inverse UQ formulation, with systematic and rigorously derived metamodels constructed by Gaussian Process (GP), and proposed an improved modular Bayesian approach that can avoid extrapolating the model discrepancy that is learnt from the inverseUQ domain to the validation/prediction domain. (via Semantic Scholar)
Source: ORCID
Added: July 5, 2019

2018 journal article

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.

By: X. Wu*, T. Kozlowski* & H. Meidani*

Contributors: X. Wu*, T. Kozlowski* & H. Meidani*

author keywords: Inverse uncertainty quantification; Metamodel; Kriging; Nuclear fuel performance analysis; Principal component analysis
TL;DR: In this paper, inverse Uncertainty Quantification (UQ) under the Bayesian framework is applied to BISON code FGR model based on Riso-AN3 time series experimental data and the posterior distributions for the uncertain input factors can be used to replace the expert specifications for future uncertainty/sensitivity analysis. (via Semantic Scholar)
UN Sustainable Development Goal Categories
7. Affordable and Clean Energy (OpenAlex)
Source: ORCID
Added: July 5, 2019

2018 conference paper

On the connection between sensitivity and identifiability for inverse uncertainty quantification

Transactions of the American Nuclear Society, 118, 411–414. http://www.scopus.com/inward/record.url?eid=2-s2.0-85062995468&partnerID=MN8TOARS

By: X. Wu, K. Shirvan & T. Kozlowski

Contributors: X. Wu, K. Shirvan & T. Kozlowski

Source: ORCID
Added: July 5, 2019

2018 conference paper

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. http://www.scopus.com/inward/record.url?eid=2-s2.0-85060860913&partnerID=MN8TOARS

By: Y. Che, X. Wu, G. Pastore, J. Hales & K. Shirvan

Contributors: Y. Che, X. Wu, G. Pastore, J. Hales & K. Shirvan

Source: ORCID
Added: July 5, 2019

2018 conference paper

System code evaluation of accident tolerant claddings during BWR station blackout accident

Transactions of the American Nuclear Society, 119, 444–447. http://www.scopus.com/inward/record.url?eid=2-s2.0-85060862292&partnerID=MN8TOARS

By: X. Wu & K. Shirvan

Contributors: X. Wu & K. Shirvan

Source: ORCID
Added: July 5, 2019

2018 conference paper

Validating trace void fraction predictive capability using the quantitative area validation metric

Transactions of the American Nuclear Society, 118, 423–426. http://www.scopus.com/inward/record.url?eid=2-s2.0-85062957450&partnerID=MN8TOARS

By: X. Wu, K. Shirvan & T. Kozlowski

Contributors: X. Wu, K. Shirvan & T. Kozlowski

Source: ORCID
Added: July 5, 2019

2017 journal article

Inverse uncertainty quantification of TRACE physical model parameters using sparse gird stochastic collocation surrogate model

Nuclear Engineering and Design, 319, 185–200.

By: X. Wu*, T. Mui*, G. Hu*, H. Meidani* & T. Kozlowski*

Contributors: X. Wu*, T. Mui*, G. Hu*, H. Meidani* & T. Kozlowski*

TL;DR: This research solves the problem of lack of uncertainty information for TRACE physical model parameters for the closure relations by replacing such ad-hoc expert judgment with inverse Uncertainty Quantification (UQ) based on OECD/NRC BWR Full-size Fine-Mesh Bundle Tests (BFBT) benchmark steady-state void fraction data. (via Semantic Scholar)
Source: ORCID
Added: July 5, 2019

2017 journal article

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.

By: X. Wu* & T. Kozlowski*

Contributors: X. Wu* & T. Kozlowski*

Source: ORCID
Added: July 5, 2019

2017 conference paper

Kriging-based inverse uncertainty quantification of BISON fission gas release model

Transactions of the American Nuclear Society, 116, 629–632. http://www.scopus.com/inward/record.url?eid=2-s2.0-85033468956&partnerID=MN8TOARS

By: X. Wu & T. Kozlowski

Contributors: X. Wu & T. Kozlowski

Source: ORCID
Added: July 5, 2019

2017 conference paper

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. http://www.scopus.com/inward/record.url?eid=2-s2.0-85051935457&partnerID=MN8TOARS

By: C. Wang, X. Wu & T. Kozlowski

Contributors: C. Wang, X. Wu & T. Kozlowski

Source: ORCID
Added: July 5, 2019

2017 conference paper

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. http://www.scopus.com/inward/record.url?eid=2-s2.0-85051987392&partnerID=MN8TOARS

By: C. Wang, X. Wu & T. Kozlowski

Contributors: C. Wang, X. Wu & T. Kozlowski

Source: ORCID
Added: July 5, 2019

2015 journal article

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.

By: X. Wu* & T. Kozlowski*

Contributors: X. Wu* & T. Kozlowski*

author keywords: Monte Carlo; System thermal-hydraulics; Coupled simulation; Uncertainty quantification
UN Sustainable Development Goal Categories
7. Affordable and Clean Energy (OpenAlex)
Source: ORCID
Added: July 5, 2019

2015 conference paper

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. http://www.scopus.com/inward/record.url?eid=2-s2.0-84949522107&partnerID=MN8TOARS

By: M. Rose, T. Downar, X. Wu & T. Kozlowski

Contributors: M. Rose, T. Downar, X. Wu & T. Kozlowski

Source: ORCID
Added: July 5, 2019

2015 journal article

Neutronics and fuel performance evaluation of accident tolerant FeCrAl cladding under normal operation conditions

Annals of Nuclear Energy, 85, 763–775.

By: X. Wu*, T. Kozlowski* & J. Hales*

Contributors: X. Wu*, T. Kozlowski* & J. Hales*

author keywords: FeCrAl; Accident Tolerant Fuel
Source: ORCID
Added: July 5, 2019

2014 conference paper

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. http://www.scopus.com/inward/record.url?eid=2-s2.0-84907077778&partnerID=MN8TOARS

By: X. Wu & T. Kozlowski

Contributors: X. Wu & T. Kozlowski

Source: ORCID
Added: July 5, 2019

2014 conference paper

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. http://www.scopus.com/inward/record.url?eid=2-s2.0-84907085456&partnerID=MN8TOARS

By: X. Wu, T. Kozlowski & B. Heuser

Contributors: X. Wu, T. Kozlowski & B. Heuser

Source: ORCID
Added: July 5, 2019

2014 report

Neutronics and Fuel Performance Evaluation of Accident Tolerant Fuel under Normal Operation Conditions

By: X. Wu*, P. Sabharwall & J. Hales

Sources: Crossref, NC State University Libraries
Added: June 24, 2023

2014 conference paper

Uncertainty quantification for coupled Monte Carlo and thermal-hydraulics codes

Transactions of the American Nuclear Society, 110, 189–191. http://www.scopus.com/inward/record.url?eid=2-s2.0-84904692027&partnerID=MN8TOARS

By: X. Wu & T. Kozlowski

Contributors: X. Wu & T. Kozlowski

Source: ORCID
Added: July 5, 2019

2013 conference paper

Engineered Zircaloy cladding modifications for improved accident tolerance of LWR fuel: A summary

LWR Fuel Performance Meeting, Top Fuel 2013, 1, 56–58. http://www.scopus.com/inward/record.url?eid=2-s2.0-84902344678&partnerID=MN8TOARS

By: B. Heuser, T. Kozlowski & W. Xu

Contributors: B. Heuser, T. Kozlowski & W. Xu

Source: ORCID
Added: July 5, 2019

Employment

Updated: June 29th, 2019 11:25

2019 - present

North Carolina State University Raleigh, NC, US
Assistant Professor Department of Nuclear Engineering

2017 - 2019

Massachusetts Institute of Technology Cambridge, MA, US
Postdoctoral Associate Department of Nuclear Science and Engineering

Education

Updated: October 11th, 2019 14:14

2013 - 2017

University of Illinois at Urbana-Champaign Urbana, IL, US
Ph.D Department of Nuclear, Plasma and Radiological Engineering

2011 - 2013

University of Illinois at Urbana-Champaign Urbana, IL, US
M.S. in Nuclear Engineering Department of Nuclear, Plasma and Radiological Engineering

2007 - 2011

Shanghai Jiao Tong University Shanghai, CN
B.S in Nuclear Engineering and Technology School of Mechanical Engineering

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