https://doi.org/10.1051/epjconf/202124706029
FEW-GROUP CROSS SECTIONS MODELING BY ARTIFICIAL NEURAL NETWORKS
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 modeling of homogenized few-group cross sections by Artificial Neural Networks (ANN). A comprehensive sensitivity study on data normalization, network architectures and training hyper-parameters specifically for Deep and Shallow Feed Forward ANN is presented. The optimal models in terms of reduction in the library size and training time are compared to multi-linear interpolation on a Cartesian grid. The use case is provided by the OECD-NEA Burn-up Credit Criticality Benchmark [1]. The Pytorch [2] machine learning framework is used.
Key words: Homogenized Cross Sections / Machine Learning / Artificial Neural Networks / Supervised 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.
