https://doi.org/10.1051/epjconf/202124715016
MODELING NUCLEAR DATA UNCERTAINTIES USING DEEP NEURAL NETWORKS
Department of Nuclear, Plasma, and Radiological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
radaide2@illinois.edu
deanrp2@illinois.edu
txk@illinois.edu
* Corresponding Author: radaide2@illinois.edu (M. I. Radaideh)
Published online: 22 February 2021
A new concept using deep learning in neural networks is investigated to characterize the underlying uncertainty of nuclear data. Analysis is performed on multi-group neutron cross-sections (56 energy groups) for the GODIVA U-235 sphere. A deep model is trained with cross-validation using 1000 nuclear data random samples to fit 336 nuclear data parameters. Although of the very limited sample size (1000 samples) available in this study, the trained models demonstrate promising performance, where a prediction error of about 166 pcm is found for keff in the test set. In addition, the deep model’s sensitivity and uncertainty are validated. The comparison of importance ranking of the principal fast fission energy groups with adjoint methods shows fair agreement, while a very good agreement is observed when comparing the global keff uncertainty with sampling methods. The findings of this work shall motivate additional efforts on using machine learning to unravel complexities in nuclear data research.
Key words: Nuclear Data / DNN / Sensitivity Analysis / Uncertainty Quantification / SCALE/Sampler
© 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.
