| Issue |
EPJ Web Conf.
Volume 247, 2021
PHYSOR2020 – International Conference on Physics of Reactors: Transition to a Scalable Nuclear Future
|
|
|---|---|---|
| Article Number | 06012 | |
| Number of page(s) | 8 | |
| Section | Advanced Modelling and Simulation | |
| DOI | https://doi.org/10.1051/epjconf/202124706012 | |
| Published online | 22 February 2021 | |
https://doi.org/10.1051/epjconf/202124706012
FEW-GROUP CROSS SECTIONS LIBRARY BY ACTIVE LEARNING WITH SPLINE KERNELS
CEA, DEN, DM2S Service d’études des réacteurs et de mathématiques appliquées, Université Paris-Saclay F-91191 Gif-sur-Yvette, France
esteban.szames@cea.fr
karim.ammar@cea.fr
daniele.tomatis@cea.fr
jean-marc.martinez@cea.fr
Published online: 22 February 2021
This work deals with the representation of homogenized few-groups cross sections libraries by machine learning. A Reproducing Kernel Hilbert Space (RKHS) is used for different Pool Active Learning strategies to obtain an optimal support. Specifically a spline kernel is used and results are compared to multi-linear interpolation as used in industry, discussing the reduction of the library size and of the overall performance. A standard PWR fuel assembly provides the use case (OECD-NEA Burn-up Credit Criticality Benchmark [1]).
Key words: Homogenized Cross Sections / Machine Learning / Kernel Methods / Pool Active Learning
© The Authors, published by EDP Sciences, 2021
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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