A variance deconvolution estimator for efficient uncertainty quantification in Monte Carlo radiation transport applications-Reference-Cited by-同舟云学术

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A variance deconvolution estimator for efficient uncertainty quantification in Monte Carlo radiation transport applications

Published:2024-06 Issue: Volume:319 Page:108958
ISSN:0022-4073
Container-title:Journal of Quantitative Spectroscopy and Radiative Transfer
language:en
Short-container-title:Journal of Quantitative Spectroscopy and Radiative Transfer

Author:

Clements Kayla B.^ORCID,Geraci Gianluca,Olson Aaron J.,Palmer Todd S.

Funder

National Nuclear Security Administration Office of Defense Programs

Publisher

Elsevier BV

Reference49 articles.

1. Assessing the reliability of complex models: Mathematical and statistical foundations of verification, validation, and uncertainty quantification;National Research Council,2012

2. Overview of ASME V&V 20-2009 standard for verification and validation in computational fluid mechanics and heat transfer;Dowding,2016

3. Uncertainty and sensitivity analysis for models of complex systems;Helton,2008

4. Handbook of uncertainty quantification;Ghanem,2017

5. Global sensitivity analysis: The primer;Saltelli,2008

Cited by 2 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. Monte Carlo / Dynamic Code (MC/DC): An accelerated Python package for fully transient neutron transport and rapid methods development;Journal of Open Source Software;2024-04-09

2. ANALYSIS OF THE CHALLENGES IN DEVELOPING SAMPLE-BASED MULTIFIDELITY ESTIMATORS FOR NONDETERMINISTIC MODELS;International Journal for Uncertainty Quantification;2024

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