Xu Wu

Development of physics-consistent conditional diffusion model to overcome data scarcity in critical heat flux

Alsafadi, F., Akins, A., & Wu, X. (2026, February 14). Energy and AI.

Nuclear Data Adjustment for Nonlinear Applications in the OECD/NEA WPNCS SG14 Benchmark—A Bayesian Inverse UQ-Based Approach for Data Assimilation

Brady, C., & Wu, X. (2026, February 9). Nuclear Science and Engineering, Vol. 2.

An investigation on machine learning predictive accuracy improvement and uncertainty reduction using VAE-based data augmentation

Alsafadi, F., Yaseen, M., & Wu, X. (2025, September 11). Nuclear Engineering and Design, Vol. 445.

Bayesian Calibration and Sensitivity Analysis of Rayleigh Scattering Fiber Optic Distributed Temperature Sensing in Water Flow Loop

Kohler, L., Lisowski, D., Weathered, M., Wu, X., & Heifetz, A. (2025, August 20). Nuclear Science and Engineering, Vol. 8.

Data-driven prediction and uncertainty quantification of PWR crud-induced power shift using convolutional neural networks

Furlong, A., Alsafadi, F., Palmtag, S., Godfrey, A., & Wu, X. (2025, January 11). Energy, Vol. 316.

Monte Carlo Dropout Uncertainty Quantification of Long Short-Term Memory Autoencoder Anomaly Detection in a Liquid Sodium Cold Trap

Akins, A., Kultgen, D., Wu, X., & Heifetz, A. (2025, September 26). Nuclear Technology, Vol. 9.

Physics-based hybrid machine learning for critical heat flux prediction with uncertainty quantification

Furlong, A., Zhao, X., Salko, R. K., & Wu, X. (2025, July 15). Applied Thermal Engineering, Vol. 279.

Predicting critical heat flux with uncertainty quantification and domain generalization using conditional variational autoencoders and deep neural networks

Alsafadi, F., Furlong, A., & Wu, X. (2025, April 28). Annals of Nuclear Energy, Vol. 220.

Sensitivity and uncertainty analysis in pebble-bed reactors: A study using the High-Temperature Code Package (HCP)

Yaseen, M., Sadek, A., Osman, W., Altahhan, M., Wu, X., Avramova, M., & Ivanov, K. (2025, April 10). Annals of Nuclear Energy, Vol. 219.

Uncertainty Quantification for Data-Driven Machine Learning Models in Nuclear Engineering Applications: Where We Are and What Do We Need?

Wu, X., Moloko, L. E., Bokov, P. M., Delipei, G. K., Kaizer, J., & Ivanov, K. N. (2025, October 22). Nuclear Science and Engineering.

A systematic approach for the adequacy analysis of a set of experimental databases: Application in the framework of the ATRIUM activity

Baccou, J., Glantz, T., Ghione, A., Sargentini, L., Fillion, P., Damblin, G., … Adorni, M. (2024, March 2). Nuclear Engineering and Design, Vol. 421.

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

Akins, A., Furlong, A., Kohler, L., Clifford, J., Brady, C., Alsafadi, F., & Wu, X. (2024, April 1). Nuclear Engineering and Design, Vol. 423.

Clustering and uncertainty analysis to improve the machine learning-based predictions of SAFARI-1 control follower assembly axial neutron flux profiles

Moloko, L. E., Bokov, P. M., Wu, X., & Ivanov, K. N. (2024, May 28). Annals of Nuclear Energy, Vol. 206.

Design considerations and Monte Carlo criticality analysis of spiral plate heat exchangers for Molten Salt Reactors

Brady, C., Murray, W., Moss, L., Zino, J., Saito, E., & Wu, X. (2024, May 20). Progress in Nuclear Energy, Vol. 173.

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

Xie, Z., Yaseen, M., & Wu, X. (2024, January 5). Computer Methods in Applied Mechanics and Engineering, Vol. 420.

Hierarchical Bayesian modeling for Inverse Uncertainty Quantification of system thermal-hydraulics code using critical flow experimental data

Xie, Z., Wang, C., & Wu, X. (2024, December 3). International Journal of Heat and Mass Transfer, Vol. 239.

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

Alsafadi, F., & Wu, X. (2023, October 31). Nuclear Engineering and Design, Vol. 415.

Fast and accurate reduced-order modeling of a MOOSE-based additive manufacturing model with operator learning

Yaseen, M., Yushu, D., German, P., & Wu, X. (2023, October 21). The International Journal of Advanced Manufacturing Technology, Vol. 129, pp. 3123–3139.

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

In arXiv (Cornell University) (ArXiv Preprint No. 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, March 22). Annals of Nuclear Energy.

Scalable Inverse Uncertainty Quantification by Hierarchical Bayesian Modeling and Variational Inference

Wang, C., Wu, X., Xie, Z., & Kozlowski, T. (2023, November 20). Energies, Vol. 16.

Quantification of Deep Neural Network Prediction Uncertainties for VVUQ of Machine Learning Models

Yaseen, M., & Wu, X. (2022, November 2). Nuclear Science and Engineering, Vol. 11.

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, October 4). Nuclear Engineering and Design, Vol. 12.

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. (2021, November 11). Annals of Nuclear Energy, Vol. 1.

Structural Health Monitoring of Microreactor Safety Systems Using Convolutional Neural Networks

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, August 31). Nuclear Engineering and Design, Vol. 11.

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. (2020, December 17). Annals of Nuclear Energy, Vol. 153.

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

Lu, C., Wu, Z., & Wu, X. (2020, October 16). Nuclear Technology, Vol. 10, pp. 1–19.

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

Jin, Y., Wu, X., & Shirvan, K. (2020, August 19). Nuclear Engineering and Design, Vol. 368.

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

Wu, X., Shirvan, K., & Kozlowski, T. (2019, June 21). Journal of Computational Physics, Vol. 396, pp. 12–30.

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

Gaussian Process–Based Inverse Uncertainty Quantification for TRACE Physical Model Parameters Using Steady-State PSBT Benchmark

Wang, C., Wu, X., & Kozlowski, T. (2018, August 10). Nuclear Science and Engineering, Vol. 193, pp. 100–114.

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, June 19). Nuclear Engineering and Design, Vol. 335, pp. 417–431.

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

Wu, X., Kozlowski, T., Meidani, H., & Shirvan, K. (2018, June 19). Nuclear Engineering and Design, Vol. 335, pp. 339–355.

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

Sensitivity and uncertainty analysis for fuel performance evaluation of Cr 2 O 3 -doped UO 2 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

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

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

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

Wu, X., Mui, T., Hu, G., Meidani, H., & Kozlowski, T. (2017, May 20). Nuclear Engineering and Design, Vol. 319, pp. 185–200.

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

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. (2017, October 4). Reliability Engineering & System Safety, Vol. 169, pp. 422–436.

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

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

Inverse uncertainty quantification of reactor simulations under the Bayesian framework using surrogate models constructed by polynomial chaos expansion

Wu, X., & Kozlowski, T. (2016, December 18). Nuclear Engineering and Design, Vol. 313, pp. 29–52.

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

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

Wu, X., Kozlowski, T., & Hales, J. D. (2015, July 2). Annals of Nuclear Energy, Vol. 85, pp. 763–775.

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

Coupling of system thermal–hydraulics and Monte-Carlo code: Convergence criteria and quantification of correlation between statistical uncertainty and coupled error

Wu, X., & Kozlowski, T. (2014, September 16). Annals of Nuclear Energy, Vol. 75, pp. 377–387.

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

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

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

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