Bayesian inverse uncertainty quantification of the physical model parameters for the spallation neutron source first target station-Reference-Cited by-同舟云学术

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Bayesian inverse uncertainty quantification of the physical model parameters for the spallation neutron source first target station

Published:2022-05 Issue: Volume:36 Page:105414
ISSN:2211-3797
Container-title:Results in Physics
language:en
Short-container-title:Results in Physics

Author:

Radaideh Majdi I.^ORCID,Lin Lianshan,Jiang Hao,Cousineau Sarah

Funder

Oak Ridge National Laboratory

U.S. Department of Energy

US Department of Energy Office of Science

US Department of Energy Basic Energy Sciences

Publisher

Elsevier BV

Subject

General Physics and Astronomy

Reference38 articles.

1. Uncertainty quantification: theory, implementation, and applications, Vol. 12;Smith,2013

2. Bayesian linear regression with sparse priors;Castillo;Ann Statist,2015

3. Bayesian neural networks and density networks;MacKay;Nucl Instrum Methods Phys Res A,1995

4. Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian process, part 1: Theory;Wu;Nucl Eng Des,2018

5. The use of machine learning for inverse uncertainty quantification in TRACE code based on Marviken experiment;Domitr;Nucl Eng Des,2021

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