Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian process, Part 1: Theory-Reference-Cited by-同舟云学术

Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian process, Part 1: Theory

Published:2018-08 Issue: Volume:335 Page:339-355
ISSN:0029-5493
Container-title:Nuclear Engineering and Design
language:en
Short-container-title:Nuclear Engineering and Design

Author:

Wu Xu,Kozlowski Tomasz^ORCID,Meidani Hadi^ORCID,Shirvan Koroush

Publisher

Elsevier BV

Subject

Mechanical Engineering,Waste Management and Disposal,Safety, Risk, Reliability and Quality,General Materials Science,Nuclear Energy and Engineering,Nuclear and High Energy Physics

Reference75 articles.

1. Adams, B.M., et al., 2016. DAKOTA, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.4 User’s Manual. Sandia National Laboratories, Tech. Rep. SAND2014-4633.

2. Quantification of model uncertainty: calibration, model discrepancy, and identifiability;Arendt;J. Mech. Des.,2012

3. Diagnostics for Gaussian process emulators;Bastos;Technometrics,2009

4. A framework for validation of computer models;Bayarri;Technometrics,2007

5. Implementation and tests of low-discrepancy sequences;Bratley;ACM Trans. Model. Comput. Simul. (TOMACS),1992

Cited by 88 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Full-field experiment-aided virtual modelling framework for inverse-based stochastic prediction of structures with elastoplasticity;Computer Methods in Applied Mechanics and Engineering;2024-11

2. Development of higher order deterministic approaches of forward and inverse uncertainty quantification for nonlinear engineering systems;Progress in Nuclear Energy;2024-11

3. Choked-flow model parameter uncertainty determination using hierarchical calibration;Annals of Nuclear Energy;2024-11

4. Security Constrained Uncertainty Interval Estimation using Sensitivity Trajectories in Dynamical Systems;2024 American Control Conference (ACC);2024-07-10

5. Analyzing the effects of various isotropic and anisotropic kernels on critical heat flux prediction using Gaussian process regression;Engineering Applications of Artificial Intelligence;2024-07