Mahmoud Qasim Yaseen

College of Engineering

Works (3)

Updated: April 11th, 2024 05:00

2024 journal article

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


By: Z. Xie n, M. Yaseen n & X. Wu n

author keywords: Bayesian inverse UQ; Functional alignment; Functional PCA; Neural networks
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 journal article

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


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
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 4). 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
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)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: November 3, 2022

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