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- W3131111245 abstract "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." @default.
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- W3131111245 date "2021-01-01" @default.
- W3131111245 modified "2023-10-01" @default.
- W3131111245 title "FEW-GROUP CROSS SECTIONS MODELING BY ARTIFICIAL NEURAL NETWORKS" @default.
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- W3131111245 doi "https://doi.org/10.1051/epjconf/202124706029" @default.
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