Bulletin of the American Physical Society
66th Annual Meeting of the APS Division of Plasma Physics
Monday–Friday, October 7–11, 2024; Atlanta, Georgia
Session TO07: Joint ICF & MFE: Machine Learning and Data Science Technologies
9:30 AM–12:18 PM,
Thursday, October 10, 2024
Hyatt Regency
Room: Hanover FG
Chair: Aidan Crilly, Imperial College London
Abstract: TO07.00013 : Comparison of Mo versus W for Double Shell Target Capsules using Machine Learning Optimization*
12:06 PM–12:18 PM
Presenter:
Nomita Vazirani
(Los Alamos National Lab)
Authors:
Nomita Vazirani
(Los Alamos National Lab)
Ryan F Sacks
(LANL)
Brian Michael Haines
(Los Alamos National Laboratory)
Michael J Grosskopf
(Los Alamos National Lab)
David Stark
(William & Mary)
Paul A Bradley
(Los Alamos Natl Lab)
Eric N Loomis
(Los Alamos Natl Lab)
Elizabeth Catherine Merritt
(Los Alamos National Laboratory)
Harry Francis Robey
(Los Alamos National Laboratory)
References
[1] D. Montgomery, et al., “Design considerations for indirectly driven double shell capsules,” Physics of Plasmas 25, 092706 (2018).
[2] N. Vazirani, et al. “Coupling 1d xrage simulations with machine learning for graded inner shell design optimization in double shell capsules,” Physics of Plasmas 28, 122709 (2021).
[3] N. Vazirani, et al. "Coupling multi-fidelity xRAGE with machine learning for graded inner shell design optimization in double shell capsules." Physics of Plasmas 30.6 (2023).
[4] N. Vazirani, et al. "Bayesian batch optimization for molybdenum versus tungsten inertial confinement fusion double shell target design." Statistical Analysis and Data Mining: The ASA Data Science Journal 17.3 (2024): e11698.
[5]M. Gittings, et al. “The RAGE Radiation-Hydrodynamic Code,” Computational Science Discovery 1 (2008), https://doi.org/10.1088/1749-4699/1/1/015005.
*This work was performed at Los Alamos National Laboratory (LANL) under the U.S. Department of Energy Contract No.89233218NCA00000.
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