Conferences

Journals
Conferences
Transactions

Refereed Full-length Conference Papers

  1. Alsafadi, F., Furlong, A., and Wu, X. (2024). Prediction and Uncertainty Quantification of Critical Heat Flux – A Comparison Between Generative Conditional VAEs and DNN. In Proceedings of the 2024 Advances in Thermal Hydraulics (ATH 2024). Orlando, FL, November 17–21, 2024
  2. Furlong, A. and Wu, X. (2024). Improving Machine Learning-based Critical Heat Flux Predictions in Data Scarce Rectangular Channels with Transfer Learning. In Proceedings of the 2024 Advances in Thermal Hydraulics (ATH 2024). Orlando, FL, November 17–21, 2024
  3. Alsafadi, F. and Wu, X. (2024). Data Augmentation of Nuclear Critical Heat Flux Experimental Data with Conditional Variational Autoencoders. In Proceedings of the 14th International Topical Meeting on Nuclear Reactor Thermal Hydraulics, Operation and Safety (NUTHOS-14). Vancouver, British Columbia, Canada, August 25-28, 2024
  4. Akins, A., Kultgen, D., Wu, X., and Heifetz, A. (2024). Uncertainty Quantification of Long Short-Term Memory Autoencoder for Monitoring of Liquid Sodium Cold Trap. In Proceedings of the 2024 Best Estimate Plus Uncertainty International Conference (BEPU 2024). Lucca, Italy, May 19-24, 2024
  5. Kohler, L., Lisowski, D., Wu, X., and Heifetz, A. (2024). Bayesian Calibration of Fiber Optic Distributed Temperature Sensing in a Thermal Mixing Tee. In Proceedings of the 2024 Best Estimate Plus Uncertainty International Conference (BEPU 2024). Lucca, Italy, May 19-24, 2024
  6. Alsafadi, F., Yaseen, M., and Wu, X. (2024). Uncertainty Quantification and Improved Neural Networks Predictions using Data Augmentation by Variational Autoencoders. In Proceedings of the 2024 Best Estimate Plus Uncertainty International Conference (BEPU 2024). Lucca, Italy, May 19-24, 2024
  7. Xie, Z., Wang, C., and Wu, X. (2024). Hierarchical Bayesian Inverse Uncertainty Quantification with Application to the ATRIUM project. In Proceedings of the 2024 Best Estimate Plus Uncertainty International Conference (BEPU 2024). Lucca, Italy, May 19-24, 2024
  8. Wu, X., Moloko, L., Bokov, P., Delipei, G., Kaiser, J., and Ivanov, K. (2024). Elucidating the Uncertainties Introduced by Data-Driven Machine Learning Models. In Proceedings of the 2024 Best Estimate Plus Uncertainty International Conference (BEPU 2024). Lucca, Italy, May 19-24, 2024
  9. Furlong, A., Alsafadi, F., Palmtag, S., Godfrey, A., Hayes, S., and Wu, X. (2024). Predicting PWR Fuel Assembly CIPS Susceptibility with Convolutional Neural Networks: Performance and Uncertainty Quantification. In Proceedings of the International Conference on Physics of Reactors (PHYSOR 2024). San Francisco, CA, USA, April 21-24, 2024
  10. Wu, X., Delipei, G., Avramova, M., Ivanov, K., and Buss, O. (2023). Introducing the OECD/NEA WPRS Benchmark on Artificial Intelligence and Machine Learning for Scientific Computing in Nuclear Engineering. In Proceedings of the 20th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-20).Washington, D.C., USA, August 20-25, 2023
  11. Alsafadi, F. and Wu, X. (2023). Deep Generative Modeling for Augmentation of the Steady-state Void Fraction Dataset in the BFBT Benchmark. In Proceedings of the 20th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-20).Washington, D.C., USA, August 20-25, 2023
  12. Yaseen, M., Xie, Z., and Wu, X. (2023). Uncertainty Quantification of Deep Neural Network Predictions for Time-dependent Responses with Functional PCA. In Proceedings of the 20th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-20).Washington, D.C., USA, August 20-25, 2023
  13. Xie, Z. and Wu, X. (2023). Neural Networks and Functional Alignment-based Bayesian Inverse UQ using FEBA Reflood Experiment Data. In Proceedings of the 20th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-20). Washington, D.C., USA, August 20-25, 2023
  14. Godbole, C., Delipei, G., Wu, X., Avramova, M., and Rohatgi, U. (2023). Prediction of Departure from Nucleate Boiling Power using ANN and PIML Algorithms. In Proceedings of the 20th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-20). Washington, D.C., USA, August 20-25, 2023
  15. Ghione, A., Sargentini, L., Damblin, G., Fillion, P., Baccou, J., Sueur, R., Iooss, B., Petruzzi, A., Zeng, K., Zhang, J., Havet, M., Mendizábal, R., Skorek, T., Wu, X., Freixa Terradas, J., and Adorni, M. (2023). Applying the SAPIUM guideline for Input Uncertainty Quantification: the ATRIUM project. In Proceedings of the 20th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-20).Washington, D.C., USA, August 20-25, 2023
  16. Moloko, L., Bokov, P., Wu, X., and Ivanov, K. (2023). Improving SAFARI-1 Control Follower Assembly Axial Flux Prediction by Combining Supervised and Unsupervised Machine Learning. In Proceedings of the 2023 International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C 2023). Niagara Falls, Ontario, Canada, August 13-17, 2023
  17. Yaseen, M., Yushu, D., German, P., and Wu, X. (2023). Reduced Order Modeling of a Moose-based Advanced Manufacturing Model with Operator Learning. In Proceedings of the 2023 International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C 2023). Niagara Falls, Ontario, Canada, August 13-17, 2023
  18. Xie, Z. and Wu, X. (2023). Bayesian Estimation of a Machine Learning-based Representation of Model Discrepancy. In Proceedings of the 2023 International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C 2023). Niagara Falls, Ontario, Canada, August 13-17, 2023
  19. Brady, C., Murray, W., Moss, L., Zino, J., Saito, E., and Wu, X. (2023). Criticality Safety Analysis of a Spiral Heat Exchanger for Molten Salt Reactors. In Proceedings of the 2023 International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C 2023). Niagara Falls, Ontario, Canada, August 13-17, 2023
  20. Godbole, C., Delipei, G., Wu, X., Avramova, M., and Rohatgi, U. (2022). Machine Learning-based Prediction of Departure from Nucleate Boiling Power for PSBT Benchmark. In Proceedings of the Advances in Thermal Hydraulics (ATH 2022). Anaheim, CA, USA, June 12–16, 2022
  21. Moloko, L., Bokov, P., Wu, X., and Ivanov, K. (2022). Quantification of Neural Networks Uncertainties with Applications to SAFARI-1 Axial Neutron Flux Profiles. In Proceedings of the International Conference on Physics of Reactors (PHYSOR) 2022. Pittsburgh, PA, USA, May 15–20, 2022
  22. Xie, Z. and Wu, X. (2022). Bayesian Inverse Uncertainty Quantification of TRACE Physical Model Parameters using FEBA Reflood Experiments. In Proceedings of the International Conference on Physics of Reactors (PHYSOR) 2022. Pittsburgh, PA, USA, May 15–20, 2022
  23. Akins, A. and Wu, X. (2022). Using Physics-Informed Neural Networks to solve a System of Coupled ODEs for a Reactivity Insertion Accident. In Proceedings of the International Conference on Physics of Reactors (PHYSOR) 2022. Pittsburgh, PA, USA, May 15–20, 2022
  24. Xie, Z. and Wu, X. (2021). A Comprehensive Framework to Improve Computer Model Simulations by Integrating Inverse Uncertainty Quantification and Validation. In Proceedings of the 2021 International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C-2021). Raleigh, NC, USA, October 3–7, 2021
  25. Xie, Z., Jiang, W., Wang, C., and Wu, X. (2021). Inverse Uncertainty Quantification of a MOOSE-based Melt Pool Model for Additive Manufacturing. In Proceedings of the 2021 International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C-2021). Raleigh, NC, USA, October 3–7, 2021
  26. Wang, C., Wu, X., and Kozlowski, T. (2019). Inverse Uncertainty Quantification by Hierarchical Bayesian Inference for TRACE Physical Model Parameters based on BFBT benchmark. In Proceedings of the 18th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-18). Portland, Oregon, USA, Aug. 18-22, 2019
  27. Che, Y., Wu, X., Li,W., Shirvan, K., Pastore, G., and Hales, J. (2018). Sensitivity and Uncertainty Analysis of Fuel Performance Assessment of Chromia-Doped Fuel during Large-Break LOCA. In Proceedings of the 2018 Light Water Reactor Fuel Performance Conference (TopFuel-2018). Prague, Czech Republic, Sep. 30 – Oct. 04, 2018
  28. Wang, C., Wu, X., and Kozlowski, T. (2018). Surrogate-based Bayesian Calibration of Thermal-Hydraulics Models based on PSBT Time-dependent Benchmark Data. In Proceedings of the ANS Best Estimate Plus Uncertainty International Conference (BEPU-2018). Real Collegio, Lucca, Italy, May 13-19, 2018
  29. Wu, X., Kozlowski, T., and Shirvan, K. (2018). Inverse Uncertainty Quantification using the Modular Bayesian Approach in the Presence of Model Discrepancy. In Proceedings of the ANS Best Estimate Plus Uncertainty International Conference (BEPU-2018). Real Collegio, Lucca, Italy, May 13-19, 2018
  30. Wang, C., Wu, X., and Kozlowski, T. (2017). Surrogate-based Inverse Uncertainty Quantification of TRACE Physical Model Parameters using Steady-State PSBT Void Fraction Data. In Proceedings of the 17th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-17). Xi’an, Shaanxi, China, Sept. 3-8, 2017
  31. Wang, C., Wu, X., and Kozlowski, T. (2017). Sensitivity and Uncertainty Analysis of TRACE Physical Model Parameters based on PSBT benchmark using Gaussian Process Emulator. In Proceedings of the 17th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-17). Xi’an, Shaanxi, China, Sept. 3-8, 2017
  32. Wu, X. and Kozlowski, T. (2017). Investigation of Adaptive Markov Chain Monte Carlo Algorithms for Inverse Uncertainty Quantification. In Proceedings of the 2017 International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C-2017). Jeju, Korea, April 16-20, 2017
  33. Wu, X., Wang, C., and Kozlowski, T. (2017). Kriging-based Surrogate Model for Uncertainty Quantification and Sensitivity Analysis. In Proceedings of the 2017 International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C-2017). Jeju, Korea, April 16-20, 2017
  34. Wu, X., Wang, C., and Kozlowski, T. (2017). Global Sensitivity Analysis of TRACE Physical Model Parameters based on BFBT benchmark. In Proceedings of the 2017 International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C-2017). Jeju, Korea, April 16-20, 2017
  35. Rose, M., Downar, T., Wu, X., and Kozlowski, T. (2015). Evaluation of Accident Tolerant FeCrAl Coating for PWR Cladding under Normal Operating Conditions with Coupled Neutron Transport and Fuel Performance. In Proceedings of the 2015 Mathematics and Computations, Supercomputing in Nuclear Applications and Monte Carlo International Conference (M&C+SNA+MC-2015). Nashville, TN, USA, April 19-23, 2015
  36. Wu, X. and Kozlowski, T. (2014). Development of a New Convergence Criterion for Monte Carlo Simulation with Thermal-Hydraulics Feedback. In Proceedings of the 2014 Physics of Reactors conferences (PHYSOR-2014). Kyoto, Japan, September 28 – October 3, 2014
  37. Wu, X., Kozlowski, T., and Heuser, B. (2014). Neutronics Analysis of Improved Accident Tolerance of LWR Fuel by Modifying Zircaloy Cladding of Fuel Pins. In Proceedings of the 2014 International Congress on Advances in Nuclear Power Plants (ICAPP-2014). Charlotte, NC, USA, April 6-9, 2014
  38. Wu, X. and Kozlowski, T. (2014). Coupling of System Thermal-Hydraulics and Monte-Carlo Method for a Consistent Thermal-Hydraulics-Reactor Physics Feedback. In Proceedings of the 2014 International Congress on Advances in Nuclear Power Plants (ICAPP-2014). Charlotte, NC, USA, April 6-9, 2014
  39. Heuser, B., Kozlowski, T., and Wu, X. (2013). Engineered Zircaloy Cladding Modifications for Improved Accident Tolerance of LWR Fuel: A Summary. In Proceedings of the 2013 LWR Fuel Performance Meeting (TopFuel-2013), pages 15–19. Charlotte, NC, USA, September 15-19, 2013

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