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Journal of Computational and Theoretical Transport Volume 47, 2018 - Issue 4-6: Proceedings of the 25th International Conference on Transport Theory, Part II: Computational and Applied Transport
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Articles

Using Machine Learning Methods to Predict Bias in Nuclear Criticality Safety

Pavel GrechanukNuclear Science and Engineering Department, Oregon State University, Corvallis, OR, USA; Correspondence[email protected]
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Michael E. RisingXCP-3 Computational Physics Group, Los Alamos National Laboratory, Los Alamos, NM, USAView further author information
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Todd S. PalmerNuclear Science and Engineering Department, Oregon State University, Corvallis, OR, USA; View further author information
Pages 552-565 | Published online: 28 Mar 2019

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Thomas G. Saller, Vishnu Nair, Andrew Till & Nathan Gibson. (2023) Using a Random Forest Model to Choose Optimized Group Structures. Nuclear Science and Engineering 197:8, pages 2117-2135.
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John Pevey, Briana Hiscox, Austin Williams, Ondřej Chvála, Vladimir Sobes & J. Wesley Hines. (2022) Gradient-Informed Design Optimization of Select Nuclear Systems. Nuclear Science and Engineering 196:12, pages 1559-1571.
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Articles from other publishers (9)

Hui Wang, Jiali Huang & Jun Su. (2023) Studying differential cross section for elastic proton scattering by a tensor model. Progress in Nuclear Energy 165, pages 104891.
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Ahmed Shama, Stefano Caruso & Dimitri Rochman. (2023) Analyses of the bias and uncertainty of SNF decay heat calculations using Polaris and ORIGEN. Frontiers in Energy Research 11.
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Paweł Domitr & Mateusz Włostowski. (2021) The use of machine learning for inverse uncertainty quantification in TRACE code based on Marviken experiment. Nuclear Engineering and Design 384, pages 111498.
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Bamidele Ebiwonjumi, Alexey Cherezov, Siarhei Dzianisau & Deokjung Lee. (2021) Machine learning of LWR spent nuclear fuel assembly decay heat measurements. Nuclear Engineering and Technology 53:11, pages 3563-3579.
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Denise Neudecker, Oscar Cabellos, Alexander R. Clark, Michael J. Grosskopf, Wim Haeck, Michal W. Herman, Jesson Hutchinson, Toshihiko Kawano, Amy E. Lovell, Ionel Stetcu, Patrick Talou & Scott Vander Wiel. (2021) Informing nuclear physics via machine learning methods with differential and integral experiments. Physical Review C 104:3.
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Jessica J. Berry, Gonzalo G. Gil-Delgado & Andrew G.S. Osborne. (2021) Classification of group structures for a multigroup collision probability model using machine learning. Annals of Nuclear Energy 160, pages 108367.
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Majdi I. Radaideh, Dean Price & Tomasz Kozlowski. (2021) MODELING NUCLEAR DATA UNCERTAINTIES USING DEEP NEURAL NETWORKS. EPJ Web of Conferences 247, pages 15016.
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D. Neudecker, M. Grosskopf, M. Herman, W. Haeck, P. Grechanuk, S. Vander Wiel, M.E. Rising, A.C. Kahler, N. Sly & P. Talou. (2020) Enhancing nuclear data validation analysis by using machine learning. Nuclear Data Sheets 167, pages 36-60.
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M. Herman, D.A. Brown, M.B. Chadwick, W. Haeck, T. Kawano, D. Neudecker, P. Talou, A. Trkov & M.C. White. (2020) New paradigm for nuclear data evaluation. EPJ Web of Conferences 239, pages 11001.
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