{"id":24,"date":"2020-07-13T13:40:43","date_gmt":"2020-07-13T17:40:43","guid":{"rendered":"https:\/\/ne.ncsu.edu\/artisans\/?page_id=24"},"modified":"2026-02-19T21:31:22","modified_gmt":"2026-02-20T02:31:22","slug":"publications","status":"publish","type":"page","link":"https:\/\/ne.ncsu.edu\/artisans\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"\n
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Journals<\/a><\/div>\n\n\n\n
Conferences<\/a><\/div>\n\n\n\n
Transactions<\/a><\/div>\n<\/div>\n\n\n\n

Refereed Journal Publications<\/strong><\/strong><\/mark><\/h4>\n\n\n\n
    \n
  1. Furlong, A., Salko, R., Zhao, X., and Wu, X. (2026). A Three-Stage Bayesian Transfer Learning Framework to Improve Predictions in Data-Scarce Domains. (Under review, arXiv preprint arXiv:2510.26541)
    https:\/\/arxiv.org\/abs\/2510.26541<\/a><\/li>\n\n\n\n
  2. Furlong, A., Zhao, X., Salko, R., and Wu, X. (2026). Deployment of Traditional and Hybrid Machine Learning for Critical Heat Flux Prediction in the CTF Thermal Hydraulics Code. (accepted, in press at Nuclear Technology)<\/li>\n\n\n\n
  3. Alsafadi, F., Akins, A., and Wu, X. (2026). Development of physics-consistent conditional diffusion model to overcome data scarcity in critical heat flux. Energy and AI, 24:100702.
    https:\/\/doi.org\/10.1016\/j.egyai.2026.100702<\/a><\/li>\n\n\n\n
  4. Brady, C. and Wu, X. (2026). Nuclear Data Adjustment for Nonlinear Applications in the OECD\/NEA WPNCS SG14 Benchmark – A Bayesian Inverse UQ-based Approach for Data Assimilation. Nuclear Science and Engineering.
    https:\/\/doi.org\/10.1080\/00295639.2025.2592175<\/a><\/li>\n\n\n\n
  5. Wu, X., Moloko, L., Bokov, P., Delipei, G., Kaizer, J., and Ivanov, K. (2025). Uncertainty Quantification for Data-Driven Machine Learning Models in Nuclear Engineering Applications: Where We Are and What Do We Need? Nuclear Science and Engineering
    https:\/\/doi.org\/10.1080\/00295639.2025.2552500<\/a><\/li>\n\n\n\n
  6. Akins, A., Kultgen, D., Wu, X., and Heifetz, A. (2025). Uncertainty Quantification through Monte Carlo Dropout for Multi-Modal Anomaly Detection in a Liquid Sodium Purification System. Nuclear Technology
    https:\/\/doi.org\/10.1080\/00295450.2025.2518613<\/a><\/li>\n\n\n\n
  7. Alsafadi, F., Yaseen, M., and Wu, X. (2025). An Investigation on Machine Learning Predictive Accuracy Improvement and Uncertainty Reduction using VAE-based Data Augmentation. Nuclear Engineering and Design, 445:114433.
    https:\/\/doi.org\/10.1016\/j.nucengdes.2025.114433<\/a><\/li>\n\n\n\n
  8. Kohler, L., Lisowski, D., Weathered, M., Wu, X., and Heifetz, A. (2025). Bayesian Calibration and Sensitivity Analysis of Fiber Optic Distributed Temperature Sensing in Water. Nuclear Science and Engineering.
    https:\/\/doi.org\/10.1080\/00295639.2025.2528506<\/a><\/li>\n\n\n\n
  9. Furlong, A., Zhao, X., Salko, R., and Wu, X. (2025). Physics-based hybrid machine learning for critical heat flux prediction with uncertainty quantification. Applied Thermal Engineering, 279:127447.
    https:\/\/doi.org\/10.1016\/j.applthermaleng.2025.127447<\/a><\/li>\n\n\n\n
  10. Alsafadi, F., Furlong, A., and Wu, X. (2025). Predicting critical heat flux with uncertainty quantification and domain generalization using conditional variational autoencoders and deep neural networks. Annals of Nuclear Energy, 220:111502.
    https:\/\/doi.org\/10.1016\/j.anucene.2025.111502<\/a><\/li>\n\n\n\n
  11. Yaseen, M., Sadek, A., Osman, W., Altahhan, M., Wu, X., Avramova, M., and Ivanov, K. (2025). Sensitivity and uncertainty analysis in pebble-bed reactors: A study using the High-Temperature Code Package (HCP). Annals of Nuclear Energy, 219:111428.
    https:\/\/doi.org\/10.1016\/j.anucene.2025.111428<\/a><\/li>\n\n\n\n
  12. Furlong, A., Alsafadi, F., Palmtag, S., Godfrey, A., and Wu, X. (2025). Data-Driven Prediction and Uncertainty Quantification of PWR Crud-Induced Power Shift Using Convolutional Neural Networks. Energy, 316:134447.
    https:\/\/doi.org\/10.1016\/j.energy.2025.134447<\/a><\/li>\n\n\n\n
  13. Xie, Z., Wang, C., and Wu, X. (2025). Hierarchical Bayesian Modeling for Inverse Uncertainty Quantification of System Thermal-Hydraulics Code using Critical Flow Experimental Data. International Journal of Heat and Mass Transfer, 239:126489.
    https:\/\/doi.org\/10.1016\/j.ijheatmasstransfer.2024.126489<\/a><\/li>\n\n\n\n
  14. Moloko, L., Bokov, P., Wu, X., and Ivanov, K. (2024). Clustering and uncertainty analysis to improve the machine learning-based predictions of SAFARI-1 control follower assembly axial neutron flux profiles. Annals of Nuclear Energy, 206:110630
    https:\/\/doi.org\/10.1016\/j.anucene.2024.110630<\/a><\/li>\n\n\n\n
  15. Brady, C., Murray,W., Moss, L., Zino, J., Saito, E., and Wu, X. (2024). Design considerations and Monte Carlo criticality analysis of spiral plate heat exchangers for Molten Salt Reactors. Progress in Nuclear Energy, 173:105266
    https:\/\/doi.org\/10.1016\/j.pnucene.2024.105266<\/a><\/li>\n\n\n\n
  16. Akins, A., Furlong, A., Kohler, L., Clifford, J., Brady, C., Alsafadi, F., and Wu, X. (2024). ARTISANS – Artificial Intelligence for Simulation of Advanced Nuclear Systems for Nuclear Fission Technology. Nuclear Engineering and Design, 423:113170
    https:\/\/doi.org\/10.1016\/j.nucengdes.2024.113170<\/a><\/li>\n\n\n\n
  17. Baccou, J., Glantz, T., Ghione, A., Sargentini, L., Fillion, P., Damblin, G., Sueur, R., Iooss, B., Fang, J., Liu, J., Yang, C., Zheng, Y., Ui, A., Saito, M., Mendiz\u00e1bal Sanz, R., Bersano, A., Mascari, F., Skorek, T., Tiborcz, L., Hirose, Y., Takeda, T., Nakamura, H., Choi, C., Heo, J., Petruzzi, A., Zeng, K., Xie, Z., Wu, X., Eguchi, H., Pangukir, F., Breijder, P., Franssen, S., Perret, G., Clifford, I., Coscai, T. M., Di Maio, F., Zio, E., Pedroni, N., Zhang, J., Freixa, J., Ciurluini, C., Giannetti, F., and Adorni, M. (2024). A systematic approach for the adequacy analysis of a set of experimental databases: Application in the framework of the ATRIUM activity. Nuclear Engineering and Design, 421:113035
    https:\/\/doi.org\/10.1016\/j.nucengdes.2024.113035<\/a><\/li>\n\n\n\n
  18. Xie, Z., Yaseen, M., and Wu, X. (2024). Functional PCA and Deep Neural Networks-based Bayesian Inverse Uncertainty Quantification with Transient Experimental Data. Computer Methods in Applied Mechanics and Engineering, 420:116721
    https:\/\/doi.org\/10.1016\/j.cma.2023.116721<\/a><\/li>\n\n\n\n
  19. Yaseen, M., Yushu, D., German, P., and Wu, X. (2023). Fast and Accurate Reduced-Order Modeling of a MOOSE-based Additive Manufacturing Model with Operator Learning. The International Journal of Advanced Manufacturing Technology, 129:3123\u20133139
    https:\/\/link.springer.com\/article\/10.1007\/s00170-023-12471-1<\/a><\/li>\n\n\n\n
  20. Wang, C., Wu, X., Xie, Z., and Kozlowski, T. (2023). Scalable Inverse Uncertainty Quantification by Hierarchical Bayesian Modeling and Variational Inference. Energies, 16(22):7664
    https:\/\/www.mdpi.com\/1996-1073\/16\/22\/7664<\/a><\/li>\n\n\n\n
  21. Alsafadi, F. and Wu, X. (2023). Deep Generative Modeling-based Data Augmentation with Demonstration using the BFBT Benchmark Void Fraction Datasets. Nuclear Engineering and Design, 415:112712
    https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0029549323005617<\/a><\/li>\n\n\n\n
  22. Moloko, L., Bokov, P., Wu, X., and Ivanov, K. (2023). Prediction and Uncertainty Quantification of SAFARI-1 Axial Neutron Flux Profiles with Neural Networks. Annals of Nuclear Energy, 188:109813
    https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0306454923001329<\/a><\/li>\n\n\n\n
  23. Yaseen, M. and Wu, X. (2023). Quantification of Deep Neural Network Prediction Uncertainties for VVUQ of Machine Learning Models. Nuclear Science and Engineering, 197:947\u2013966
    https:\/\/www.tandfonline.com\/doi\/abs\/10.1080\/00295639.2022.2123203<\/a><\/li>\n\n\n\n
  24. Xie, Z., Jiang,W., Wang, C., and Wu, X. (2022). Bayesian inverse uncertainty quantification of a MOOSE-based melt pool model for additive manufacturing using experimental data. Annals of Nuclear Energy, 165:108782
    https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0306454921006599<\/a><\/li>\n\n\n\n
  25. Wu, X., Xie, Z., Alsafadi, F., and Kozlowski, T. (2021). A comprehensive survey of inverse uncertainty quantification of physical model parameters in nuclear system thermal\u2013hydraulics codes. Nuclear Engineering and Design, 384:111460
    https:\/\/www.sciencedirect.com\/science\/article\/pii\/S002954932100412X<\/a> <\/li>\n\n\n\n
  26. Xie, Z., Alsafadi, F., and Wu, X. (2021). Towards Improving the Predictive Capability of Computer Simulations by Integrating Inverse Uncertainty Quantification and Quantitative Validation with Bayesian Hypothesis Testing. Nuclear Engineering and Design, 383:111423
    https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0029549321003757<\/a><\/li>\n\n\n\n
  27. Che, Y., Wu, X., Pastore, G., Li, W., and Shirvan, K. (2021). Application of Kriging and Variational Bayesian Monte Carlo method for improved prediction of doped UO2 fission gas release. Annals of Nuclear Energy, 153:108046
    https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0306454920307428<\/a><\/li>\n\n\n\n
  28. Lu, C., Wu, Z., and Wu, X. (2021). Enhancing the one-dimensional sfr thermal stratification model via advanced inverse uncertainty quantification methods. Nuclear Technology, 207(5):692\u2013710
    https:\/\/www.tandfonline.com\/doi\/abs\/10.1080\/00295450.2020.1805259<\/a><\/li>\n\n\n\n
  29. Jin, Y., Wu, X., and Shirvan, K. (2020). System code evaluation of near-term accident tolerant claddings during pressurized water reactor station blackout accidents. Nuclear Engineering and Design, 368:110814
    https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0029549320303083<\/a><\/li>\n\n\n\n
  30. Wu, X. and Shirvan, K. (2020). System code evaluation of near-term accident tolerant claddings during boiling water reactor short-term and long-term station blackout accidents. Nuclear Engineering and Design, 356:110362
    https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0029549319303942<\/a><\/li>\n\n\n\n
  31. Wu, X., Shirvan, K., and Kozlowski, T. (2019). Demonstration of the Relationship between Sensitivity and Identifiability for Inverse Uncertainty Quantification. Journal of Computational Physics, 396:12\u201330
    https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0021999119304401<\/a><\/li>\n\n\n\n
  32. Wang, C., Wu, X., and Kozlowski, T. (2019). Gaussian process\u2013based inverse uncertainty quantification for trace physical model parameters using steady-state PSBT benchmark. Nuclear Science and Engineering, 193(1-2):100-114
    https:\/\/www.tandfonline.com\/doi\/abs\/10.1080\/00295639.2018.1499279<\/a><\/li>\n\n\n\n
  33. Wu, X., Kozlowski, T., Meidani, H., and Shirvan, K. (2018). Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian Process, Part 2: Application to TRACE. Nuclear Engineering and Design, 335:417\u2013431
    https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0029549318306411<\/a><\/li>\n\n\n\n
  34. Wu, X., Kozlowski, T., Meidani, H., and Shirvan, K. (2018). Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian process, part 1: theory. Nuclear Engineering and Design, 335:339\u2013355
    https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0029549318306423<\/a><\/li>\n\n\n\n
  35. Wu, X., Kozlowski, T., and Meidani, H. (2018). Kriging-based Inverse Uncertainty Quantification of Nuclear Fuel Performance Code BISON Fission Gas Release Model using Time Series Measurement Data. Reliability Engineering & System Safety, 169:422\u2013436
    https:\/\/www.sciencedirect.com\/science\/article\/pii\/S095183201730532X<\/a><\/li>\n\n\n\n
  36. Wu, X., Mui, T., Hu, G., Meidani, H., and Kozlowski, T. (2017). Inverse uncertainty quantification of TRACE physical model parameters using sparse gird stochastic collocation surrogate model. Nuclear Engineering and Design, 319:185\u2013200
    https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0029549317302406<\/a><\/li>\n\n\n\n
  37. Wu, X. and Kozlowski, T. (2017). Inverse uncertainty quantification of reactor simulations under the Bayesian framework using surrogate models constructed by polynomial chaos expansion. Nuclear Engineering and Design, 313:29\u201352
    https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0029549316304824<\/a><\/li>\n\n\n\n
  38. Wu, X., Kozlowski, T., and Hales, J. D. (2015). Neutronics and fuel performance evaluation of accident tolerant FeCrAl cladding under normal operation conditions. Annals of Nuclear Energy, 85:763\u2013775
    https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0306454915003461<\/a><\/li>\n\n\n\n
  39. Wu, X. and Kozlowski, T. (2015). Coupling of system thermal\u2013hydraulics and Monte-Carlo code: Convergence criteria and quantification of correlation between statistical uncertainty and coupled error. Annals of Nuclear Energy, 75:377\u2013387
    https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0306454914004010<\/a><\/li>\n<\/ol>\n\n\n\n

    Top of page<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

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