https://doi.org/10.1051/epjconf/202430202010
Adaptive sampling of homogenized cross-sections with multi-output gaussian processes
1 Université Paris-Saclay, CEA, Service d’Études des Réacteurs et de Mathématiques Appliquées
2 EDF R&D PERICLES, 7 boulevard Gaspard Monge, 91120 Palaiseau, France
3 EDF DQI, 2 rue Ampère, 93206 Saint-Denis CEDEX, France
* e-mail: olivier.truffinet@cea.fr
Published online: 15 October 2024
In another talk submitted to this conference, we presented an efficient new framework based on multi-outputs gaussian processes (MOGP) for the interpolation of few-groups homogenized cross-sections (HXS) inside deterministic core simulators. We indicated that this methodology authorized a principled selection of interpolation points through adaptive sampling. We here develop this idea by trying simple sampling schemes on our problem. In particular, we compare sample scoring functions with and without integration of leave-one-out errors, and obtained with single-output and multi-output gaussian process models. We test these methods on a realistic PWR assembly with gadolinium-added fuel rods, comparing them with non-adaptive supports. Results are promising, as the sampling algorithms allow to significantly reduce the size of interpolation supports with almost preserved accuracy. However, they exhibit phenomena of instability and stagnation, which calls for further investigation of the sampling dynamics and trying other scoring functions for the selection of samples.
© 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.
