| Issue |
EPJ Web Conf.
Volume 302, 2024
Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo (SNA + MC 2024)
|
|
|---|---|---|
| Article Number | 09008 | |
| Number of page(s) | 10 | |
| Section | Variance Reduction Techniques | |
| DOI | https://doi.org/10.1051/epjconf/202430209008 | |
| Published online | 15 October 2024 | |
https://doi.org/10.1051/epjconf/202430209008
Robustness of zero-variance Monte Carlo games
Université Paris-Saclay, CEA, Service d’étude des réacteurs et de mathématiques appliquées, 91191 Gif-sur-Yvette, France
* e-mail: thayz.gomesferreira@cea.fr
** e-mail: alexis.jinaphanh@cea.fr
*** e-mail: davide.mancusi@cea.fr
**** e-mail: andrea.zoia@cea.fr
Published online: 15 October 2024
Zero-variance Monte Carlo games are ideal sampling strategies that provide the theoretical foundations upon which very successful variancereduction schemes are built. In this work, we explore the robustness of zerovariance games and assess in particular the impact of two common simplifications used in production Monte Carlo codes. First, using a discretized adjoint function to bias the kernels that define the stochastic process. Second, replacing the exact sampling of the flight kernel by approximate sampling strategies that are more amenable to be implemented in production Monte Carlo codes. The resulting effects on the variance and on the Figure of Merit (FoM) will be probed in the framework of a benchmark configuration based on the singlespeed two-direction transport model.
Publisher note: This article has been corrected due to problems displaying special characters on October 16, 2024.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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