A Variance-Decomposition Formula for Direct and Adjoint Monte Carlo Particle Transport Problems
Abstract
In fixed-source Monte Carlo particle-transport problems, obtaining an acceptable variance on the sought response is of paramount importance. Currently, the only rigorous tool to analyze the variance of such games is the framework of the moment equations, which is unfortunately unwieldy to use in practice. In this paper, we establish a formula that enables expressing the variance of a Monte Carlo simulation as a sum of 'variance contributions' collected throughout the underlying particletransport process. This formula applies to both direct and adjoint games, and provides a new tool to pinpoint the variance-inducing mechanisms in Monte Carlo simulations, understand common variance-reduction techniques, and even conceive new ones. We showcase the use of the variance-decomposition formula on several applications. In particular, we revisit zero-variance schemes and analyze existing variance-reduction techniques, underlining their strengths and weaknesses. A few relevant numerical examples substantiate our theoretical findings.
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BibTeX
@misc{rovel2026variancedecomposition,
title = {{A Variance-Decomposition Formula for Direct and Adjoint Monte Carlo Particle Transport Problems}},
author = {Paul Rovel and Davide Mancusi and Coline Larmier and Andrea Zoia},
howpublished = {HAL},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.39210},
eprint = {2609.39210},
archivePrefix = {arXiv},
url = {https://cea.hal.science/cea-05769453v1},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - A Variance-Decomposition Formula for Direct and Adjoint Monte Carlo Particle Transport Problems
AU - Paul Rovel
AU - Davide Mancusi
AU - Coline Larmier
AU - Andrea Zoia
PY - 2026
DA - 2026/09/29
PB - HAL
DO - 10.48550/arxiv.2609.39210
UR - https://cea.hal.science/cea-05769453v1
AB - In fixed-source Monte Carlo particle-transport problems, obtaining an acceptable variance on the sought response is of paramount importance. Currently, the only rigorous tool to analyze the variance of such games is the framework of the moment equations, which is unfortunately unwieldy to use in practice. In this paper, we establish a formula that enables expressing the variance of a Monte Carlo simulation as a sum of 'variance contributions' collected throughout the underlying particletransport process. This formula applies to both direct and adjoint games, and provides a new tool to pinpoint the variance-inducing mechanisms in Monte Carlo simulations, understand common variance-reduction techniques, and even conceive new ones. We showcase the use of the variance-decomposition formula on several applications. In particular, we revisit zero-variance schemes and analyze existing variance-reduction techniques, underlining their strengths and weaknesses. A few relevant numerical examples substantiate our theoretical findings.
ER -