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Research Articles

Using a Random Forest Model to Choose Optimized Group Structures

ORCID Icon, , ORCID Icon &
Pages 2117-2135 | Received 06 Jul 2022, Accepted 06 Oct 2022, Published online: 09 Dec 2022

References

  • S. KIRKPATRICK, C. D. GELATT JR., and M. P. VECCHI, “Optimization by Simulated Annealing,” Science, 220, 4598 (1983); https://doi.org/10.1126/science.220.4598.671.
  • R. CARR, “Simulated Annealing,” from MathWorld—A Wolfram Web Resource created by Eric W. Weisstein; https://mathworld.wolfram.com/SimulatedAnnealing.html (accessed Oct. 6, 2020).
  • F. LIANG, “Optimization Techniques—Simulated Annealing,” Towards Data Science; https://towardsdatascience.com/optimization-techniques-simulated-annealing-d6a4785a1de7 (accessed Oct. 6, 2020).
  • C. M. MARTINEZ and D. CAO, “iHorizon-Enabled Energy Management for Electrified Vehicles,” Elsevier Science & Technology, San Diego, California (2018).
  • M. C. WHITE, “The 618-, 250-, 133-, 70-Group LANL Neutron Energy-Group Structures,” LANL Internal Memorandum, X-5:MCW-05-94(U), Los Alamos National Laboratory (Sep. 2005).
  • L. BREIMAN, “Random Forests,” Mach. Learn., 45, 5 (2001); https://doi.org/10.1023/A:1010933404324.
  • M. AKBARI et al., “An Investigation for an Optimized Neutron Energy-Group Structure in Thermal Lattices Using Particle Swarm Optimization,” Ann. Nucl. Energy, 47, 53 (2012); https://doi.org/10.1016/j.anucene.2012.02.016.
  • M. AKBARI et al., “A Novel Approach to Find Optimized Neutron Energy Group Structure in MOX Thermal Lattices Using Swarm Intelligence,” Nucl. Eng. Technol., 47, 951 (2013); https://doi.org/10.5516/NET.01.2012.005.
  • C. YI and G. SJODEN, “Energy Group Structure Determination Using Particle Swarm Optimization,” Ann. Nucl. Energy, 56, 53 (2013); https://doi.org/10.1016/j.anucene.2012.12.020.
  • C. YI, G. SJODEN, and C. EDGAR, “Heuristic Optimization of Group Structures Using Physics-Based Fitness Approximation,” Ann. Nucl. Energy, 96, 389 (2016); https://doi.org/10.1016/j.anucene.2016.06.028.
  • M. MASSONE, F. GABRIELLI, and A. RINEISKI, “SIMMER Extension for Multigroup Energy Structure Search Using Genetic Algorithm with Different Fitness Functions,” Nucl. Eng. Technol., 49, 1250 (2017); https://doi.org/10.1016/j.net.2017.07.012.
  • A. TILL, N. GIBSON, and T. SALLER, “Optimizing Group Structures Using Hierarchical Division,” Proc. PHYSOR 2022, Pittsburgh, Pennsylvania, May 15–19, 2022.
  • V. NAIR et al., “Group Structure Selection with Random Forests,” Proc. PHYSOR 2022, Pittsburgh, Pennsylvania, May 15–19, 2022.
  • T. SALLER, “Simulated Annealing for Group Structure Optimization,” Proc. M&C 2021, Raleigh, North Carolina, October 3–7, 2021.
  • M. GOMEZ-FERNANDEZA et al., “Status of Research and Development of Learning-Based Approaches in Nuclear Science and Engineering: A Review,” Nucl. Eng. Des., 359, 110479 (2020); https://doi.org/10.1016/j.nucengdes.2019.110479.
  • M. TANO and J. RAGUSA, “Acceleration of Radiation Transport Solves Using Artificial Neural Networks,” CoRR abs/1906.04017, arXiv:1906.04017 (2019).
  • M. POZULP, “1D Transport Using Neural Nets, SN, and MC,” Proc. M&C 2019, Portland, Oregon, August 25–29, 2019.
  • C. CAO et al., “An Adaptive Deviation-Resistant Neutron Spectrum Unfolding Method Based on Transfer Learning,” Nucl. Eng. Technol., 52, 2452 (2020); https://doi.org/10.1016/j.net.2020.04.028.
  • P. GRECHANUK, M. E. RISING, and T. S. PALMER, “Using Machine Learning Methods to Predict Bias in Nuclear Criticality Safety,” J. Comput. Theor Transport., 47, 552 (2018); https://doi.org/10.1080/23324309.2019.1585877.
  • L. MICHAEL HUANG, “Neutronic Analysis and Optimization of the Advanced High Temperature Reactor Fuel Design Using Machine Learning,” PhD Dissertation, Georgia Institute of Technology, Nuclear and Radiological Engineering Program in the School of Mechanical Engineering (Aug. 2017).
  • W. R. D. BOYD III, “Reactor Agnostic Multi-Group Cross Section Generation for Fine-Mesh Deterministic Neutron Transport Simulations,” PhD Dissertation, Massachusetts Institute of Technology, Department of Nuclear Science and Engineering (Feb. 2017).
  • S. B. KOTSIANTIS, “Decision Trees: A Recent Overview,” Artif. Intell., 39, 261 (2013); https://doi.org/10.1007/s10462-011-9272-4.
  • G. BIAU, “Analysis of a Random Forests Model,” J. Mach. Learn. Res., 13, 1, 1063 (2012).
  • W. HAECK et al., “A Comparison of Monte Carlo and Deterministic Solvers—keff and Sensitivity Profiles,” Proc. PHYSOR 2018, Cancun, Mexico, April 22–26, 2018.
  • C. J. WERNER et al., “MCNP6.2 Release Notes,” Report LA-UR-18-20808, Los Alamos National Laboratory (2018).
  • R. E. ALCOUFFE et al., “PARTISN: A Time-Dependent, Parallel Neutral Particle Transport Code System,” Report LA-UR-17-29704, Los Alamos National Laboratory (Sep. 2020).
  • R. E. MACFARLANE et al., “The NJOY Nuclear Data Processing System, Version 2016,” Report LA-UR-17-20093, Los Alamos National Laboratory (Nov. 2019).
  • M. C. WHITE, “The TD Weight Function Revisited,” LANL Internal Memo, X–5 MCW-05-24(U), Los Alamo National Laboratory (2005).
  • “ Bagging Regressor,” Scikit-Learn; https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingRegressor.html (accessed Oct. 6, 2020).

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