Farah Raed Hussein Alsafadi

College of Engineering

Works (4)

Updated: April 5th, 2024 14:34

2023 journal article

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


By: F. Alsafadi n & X. Wu n

author keywords: Deep generative modeling; Generative adversarial networks; Normalizing flows; Variational autoencoders
TL;DR: The findings shows that DGMs have a great potential to augment scientific data in nuclear engineering, which proves effective for expanding the training dataset and enabling other DL models to be trained more accurately. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries, ORCID
Added: December 18, 2023

2022 journal article

Effect of mesh refinement on the solution of the inverse uncertainty quantification problem for transient physics


By: R. Abu Saleem*, F. Alsafadi* & N. Al-Abidah

author keywords: Inverse uncertainty quantification; Maximum likelihood estimate; Maximum A posterior estimate; Sensitivity analysis; Mesh refinement; FEBA
UN Sustainable Development Goal Categories
Source: Web Of Science
Added: January 3, 2023

2021 journal article

A comprehensive survey of inverse uncertainty quantification of physical model parameters in nuclear system thermal-hydraulics codes


By: X. Wu n, Z. Xie n, F. Alsafadi n & T. Kozlowski*

author keywords: Inverse uncertainty quantification; Calibration; Physical model parameters; Frequentist; Bayesian; Empirical
TL;DR: This review paper aims to provide a comprehensive and comparative discussion of the major aspects of the IUQ methodologies that have been used on the physical models in system thermal-hydraulics codes, including solidity, complexity, accessibility, independence, flexibility, comprehensiveness, transparency, and tractability. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: November 1, 2021

2021 journal article

Towards improving the predictive capability of computer simulations by integrating inverse Uncertainty Quantification and quantitative validation with Bayesian hypothesis testing


By: Z. Xie n, F. Alsafadi n & X. Wu n

author keywords: Inverse Uncertainty Quantification; Quantitative validation; Bayesian hypothesis testing; Bayes factor; ANS nuclear grand challenge
TL;DR: This paper proposes a comprehensive framework to integrate results from inverse UQ and quantitative validation to provide robust predictions to account for all available sources of uncertainties in a rigorous Uncertainty Quantification process. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: October 12, 2021

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