Farah Raed Hussein Alsafadi
College of Engineering
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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
NUCLEAR ENGINEERING AND DESIGN, 415.
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)
arxiv.org (repository)
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
PROGRESS IN NUCLEAR ENERGY, 152.
author keywords: Inverse uncertainty quantification; Maximum likelihood estimate; Maximum A posterior estimate; Sensitivity analysis; Mesh refinement; FEBA
UN Sustainable Development Goal Categories
11. Sustainable Cities and Communities
(Web of Science)
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
NUCLEAR ENGINEERING AND DESIGN, 384.
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)
UN Sustainable Development Goal Categories
11. Sustainable Cities and Communities
(Web of Science)
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
NUCLEAR ENGINEERING AND DESIGN, 383.
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)
UN Sustainable Development Goal Categories
11. Sustainable Cities and Communities
(Web of Science)
Sources: Web Of Science, NC State University Libraries
Added: October 12, 2021
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