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Main Authors: Geraci, Gianluca, Clements, Kayla, Olson, Aaron J
Format: Preprint
Published: 2024
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Online Access:https://arxiv.org/abs/2403.07024
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author Geraci, Gianluca
Clements, Kayla
Olson, Aaron J
author_facet Geraci, Gianluca
Clements, Kayla
Olson, Aaron J
contents In this contribution, we discuss the construction of Polynomial Chaos surrogates for Monte Carlo radiation transport applications via non-intrusive spectral projection. This contribution focuses on improvements with respect to the approach that we previously introduced in previous work. We focus on understanding the impact of re-sampling cost on the algorithm performance and provide algorithm refinements, which allow to obtain unbiased estimators for the variance, estimate the PC variability due to limited samples, and adapt the expansion. An attenuation-only test case is provided to illustrate and discuss the results.
format Preprint
id arxiv_https___arxiv_org_abs_2403_07024
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle A polynomial chaos approach for uncertainty quantification of Monte Carlo transport codes
Geraci, Gianluca
Clements, Kayla
Olson, Aaron J
Numerical Analysis
Statistics Theory
In this contribution, we discuss the construction of Polynomial Chaos surrogates for Monte Carlo radiation transport applications via non-intrusive spectral projection. This contribution focuses on improvements with respect to the approach that we previously introduced in previous work. We focus on understanding the impact of re-sampling cost on the algorithm performance and provide algorithm refinements, which allow to obtain unbiased estimators for the variance, estimate the PC variability due to limited samples, and adapt the expansion. An attenuation-only test case is provided to illustrate and discuss the results.
title A polynomial chaos approach for uncertainty quantification of Monte Carlo transport codes
topic Numerical Analysis
Statistics Theory
url https://arxiv.org/abs/2403.07024