Ziyu Xie

College of Engineering

Works (6)

Updated: April 5th, 2024 14:34

2024 journal article

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

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 420.

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

Benchmarking FFTF LOFWOS Test# 13 using SAM code: Baseline model development and uncertainty quantification

ANNALS OF NUCLEAR ENERGY, 192.

By: Y. Liu*, T. Mui*, Z. Xie n & R. Hu*

author keywords: SAM; Uncertainty quantification; Sensitivity analysis; FFTF benchmark
UN Sustainable Development Goal Categories
7. Affordable and Clean Energy (OpenAlex)
Source: Web Of Science
Added: August 28, 2023

2023 journal article

Scalable Inverse Uncertainty Quantification by Hierarchical Bayesian Modeling and Variational Inference

ENERGIES, 16(22).

By: C. Wang*, X. Wu n, Z. Xie n & T. Kozlowski*

author keywords: inverse uncertainty quantification; hierarchical Bayesian; variational inference; nuclear thermal-hydraulics
Sources: Web Of Science, NC State University Libraries
Added: January 2, 2024

2022 journal article

Bayesian inverse uncertainty quantification of a MOOSE-based melt pool model for additive manufacturing using experimental data

ANNALS OF NUCLEAR ENERGY, 165.

By: Z. Xie n, W. Jiang*, C. Wang* & X. Wu n

author keywords: Inverse uncertainty quantification; Melt pool; Additive manufacturing
Sources: Web Of Science, NC State University Libraries
Added: November 29, 2021

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.

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

NUCLEAR ENGINEERING AND DESIGN, 383.

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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