EPJ Web of Conferences (Jan 2021)

FEW-GROUP CROSS SECTIONS MODELING BY ARTIFICIAL NEURAL NETWORKS

  • Szames E.,
  • Ammar K.,
  • Tomatis D.,
  • Martinez J.M.

DOI
https://doi.org/10.1051/epjconf/202124706029
Journal volume & issue
Vol. 247
p. 06029

Abstract

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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.

Keywords