Mahmoud Qasim Yaseen

College of Engineering

Works (6)

Updated: January 23rd, 2026 07:48

2025 article

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.

By: F. Alsafadi n, M. Yaseen n & X. Wu n 

author keywords: Data augmentation; Variational autoencoders; Bayesian neural network; Uncertainty quantification; Conformal prediction
topics (OpenAlex): Image and Signal Denoising Methods; Anomaly Detection Techniques and Applications; Advanced Image Processing Techniques
Sources: Web Of Science, NC State University Libraries
Added: September 29, 2025

2025 article

Modeling of silver transport in cubic SiC: Integrating molecular dynamics, bounds averaging, and uncertainty quantification

AbdulHameed, M., Mahbuba, K., Yaseen, M., Ibrahim, A., Moneghan, D., & Beeler, B. (2025, December 10). Physical Review Materials, Vol. 12.

By: M. AbdulHameed n, K. Mahbuba n , M. Yaseen*, A. Ibrahim n, D. Moneghan* & B. Beeler n 

topics (OpenAlex): Silicon Carbide Semiconductor Technologies; Copper Interconnects and Reliability; Boron and Carbon Nanomaterials Research
Sources: NC State University Libraries, NC State University Libraries, ORCID
Added: January 19, 2026

2025 article

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.

By: M. Yaseen n, A. Sadek  n, W. Osman n, M. Altahhan n, X. Wu n , M. Avramova n , K. Ivanov n 

author keywords: High temperature code package; Uncertainty quantification; Sensitivity analysis; Gaussian process
topics (OpenAlex): Probabilistic and Robust Engineering Design; Nuclear reactor physics and engineering; Nuclear Engineering Thermal-Hydraulics
Sources: Web Of Science, NC State University Libraries
Added: May 12, 2025

2024 article

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.

By: Z. Xie n, M. Yaseen n & X. Wu n 

author keywords: Bayesian inverse UQ; Functional alignment; Functional PCA; Neural networks
topics (OpenAlex): Probabilistic and Robust Engineering Design; High Temperature Alloys and Creep; Nuclear reactor physics and engineering
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: Web Of Science, ORCID, NC State University Libraries
Added: January 25, 2024

2023 article

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.

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
topics (OpenAlex): Model Reduction and Neural Networks; Probabilistic and Robust Engineering Design; Machine Learning in Materials Science
UN Sustainable Development Goals Color Wheel
UN Sustainable Development Goal Categories
Sources: Web Of Science, NC State University Libraries
Added: April 8, 2024

2022 article

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.

By: M. Yaseen n & X. Wu n 

author keywords: Uncertainty quantification; Deep Neural Network; Monte Carlo Dropout; Deep Ensemble; Bayesian Neural Network
topics (OpenAlex): Nuclear reactor physics and engineering; Nuclear Materials and Properties; Nuclear Engineering Thermal-Hydraulics
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)
UN Sustainable Development Goals Color Wheel
UN Sustainable Development Goal Categories
Sources: Web Of Science, ORCID, NC State University Libraries
Added: November 3, 2022

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