FEW-GROUP CROSS SECTIONS MODELING BY ARTIFICIAL NEURAL NETWORKS

FEW-GROUP CROSS SECTIONS MODELING BY ARTIFICIAL NEURAL NETWORKS

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 mod...

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Bibliographic Details
Main Authors: Szames E., Ammar K., Tomatis D., Martinez J.M.
Format: Article
Language:English
Published: EDP Sciences 2021-01-01
Series:EPJ Web of Conferences
Subjects:
homogenized cross sections
machine learning
artificial neural networks
supervised learning
Online Access:https://www.epj-conferences.org/articles/epjconf/pdf/2021/01/epjconf_physor2020_06029.pdf
Description
Summary: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.
ISSN:2100-014X

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