Scaling deep learning for whole-core reactor simulation

Shriver, Forrest; Watson, Justin

doi:10.1016/j.pnucene.2022.104134

Scaling deep learning for whole-core reactor simulation

Journal Article · 01 April 2022 · Progress in Nuclear Energy

DOI:https://doi.org/10.1016/j.pnucene.2022.104134· OSTI ID:1981732

Shriver, Forrest;

Not provided.

Research Organization:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)

Sponsoring Organization:: USDOE

DOE Contract Number:: AC05-00OR22725

OSTI ID:: 1981732

Journal Information:: Progress in Nuclear Energy, Vol. 146, Issue C; ISSN 0149-1970

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

References (14)

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Stability and accuracy of 3D neutron transport simulations using the 2D/1D method in MPACT Collins, Benjamin; Stimpson, Shane; Kelley, Blake W. Journal of Computational Physics, Vol. 326 https://doi.org/10.1016/j.jcp.2016.08.022	journal	December 2016
A PWR reload optimisation code (XCore) using artificial neural networks and genetic algorithms Erdoğan, Adem; Geçkinli, Melih Annals of Nuclear Energy, Vol. 30, Issue 1 https://doi.org/10.1016/S0306-4549(02)00041-5	journal	January 2003
Sensitivity study on determining an efficient set of fuel assembly parameters in training data for designing of neural networks in hybrid genetic algorithms Gozalvez, Jose M.; Yilmaz, Serkan; Alim, Fatih Annals of Nuclear Energy, Vol. 33, Issue 5 https://doi.org/10.1016/j.anucene.2005.12.006	journal	March 2006
How Generative Adversarial Networks and Their Variants Work Hong, Yongjun; Hwang, Uiwon; Yoo, Jaeyoon ACM Computing Surveys, Vol. 52, Issue 1 https://doi.org/10.1145/3301282	journal	February 2019
Optimization layer by layer networks for in-core fuel management optimization computations in PWRs Jang, Chang Sun; Shim, Hyung Jin; Kim, Chang Hyo Annals of Nuclear Energy, Vol. 28, Issue 11 https://doi.org/10.1016/S0306-4549(00)00113-4	journal	July 2001
ImageNet classification with deep convolutional neural networks Krizhevsky, Alex; Sutskever, Ilya; Hinton, Geoffrey E. Communications of the ACM, Vol. 60, Issue 6 https://doi.org/10.1145/3065386	journal	May 2017
Gradient-based learning applied to document recognition Lecun, Y.; Bottou, L.; Bengio, Y. Proceedings of the IEEE, Vol. 86, Issue 11 https://doi.org/10.1109/5.726791	journal	January 1998
Learning Efficient Convolutional Networks through Network Slimming Liu, Zhuang; Li, Jianguo; Shen, Zhiqiang 2017 IEEE International Conference on Computer Vision (ICCV) https://doi.org/10.1109/ICCV.2017.298	conference	October 2017
Predicting neutron diffusion eigenvalues with a query-based adaptive neural architecture Lysenko, M. G.; Maldonado, G. I. IEEE Transactions on Neural Networks, Vol. 10, Issue 4 https://doi.org/10.1109/72.774221	journal	July 1999
UMAP: Uniform Manifold Approximation and Projection McInnes, Leland; Healy, John; Saul, Nathaniel Journal of Open Source Software, Vol. 3, Issue 29 https://doi.org/10.21105/joss.00861	journal	September 2018
The perceptron: A probabilistic model for information storage and organization in the brain. Rosenblatt, F. Psychological Review, Vol. 65, Issue 6 https://doi.org/10.1037/h0042519	journal	January 1958
Pathology Image Analysis Using Segmentation Deep Learning Algorithms Wang, Shidan; Yang, Donghan M.; Rong, Ruichen The American Journal of Pathology, Vol. 189, Issue 9 https://doi.org/10.1016/j.ajpath.2019.05.007	journal	September 2019
Application of Neural Network for Loading Pattern Screening of In-Core Optimization Calculations Yamamoto, Akio Nuclear Technology, Vol. 144, Issue 1 https://doi.org/10.13182/NT03-A3429	journal	October 2003

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