XU WU

Assistant Professor

North Carolina State University

United States

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About

XU WU is an Assistant Professor at North Carolina State University in the United States. His research focuses on machine learning applications in nuclear technology, including uncertainty quantification and critical heat flux prediction. Recent articles highlight his work on Bayesian modeling, deep neural networks, and data-driven methodologies to enhance predictions in thermal-hydraulics and reactor safety.

Articles (12)

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2025

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2025

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2025

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2025

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2025

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2024

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2024

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2024

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2023

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2023

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2023

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2021

Collaborators (3)

Farah Alsafadi

Oak Ridge National Laboratory

UNITED STATES

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Zeyun Wu

Assistant Professor

UNITED STATES

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Xingang Zhao

The University of Tennessee Knoxville

UNITED STATES

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XU WU

Research Interests

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Articles (12)

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

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

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

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

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

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

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

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

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

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

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

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

Collaborators (3)

Farah Alsafadi

Zeyun Wu

Xingang Zhao

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