Bulletin of the American Physical Society
64th Annual Meeting of the APS Division of Plasma Physics
Volume 67, Number 15
Monday–Friday, October 17–21, 2022; Spokane, Washington
Session TP11: Poster Session VII: In-Person, Hall A (9:30-11:00am) and Virtual Poster Presentations (11:15am-12:30pm)
MFE: FRC, RFPs etc
ICF: Fast Ignition; Diagnostics; Computational; Laser Plasma Interactions
FUND: Computation
9:30 AM - 12:30 PM
Thursday, October 20, 2022
Room: Exhibit Hall A and Online
Abstract: TP11.00093 : Using Deep Learning to Investigate Performance and Variability in Inertial Confinement Fusion Experiments
Presenter:
Michael Pokornik
(Lawrence Livermore National Laboratory, Livermore, CA)
Authors:
Michael Pokornik
(Lawrence Livermore National Laboratory, Livermore, CA)
Jim A Gaffney
(Lawrence Livermore National Laboratory, Livermore, CA)
Shahab F Khan
(Lawrence Livermore National Laboratory, Livermore, CA)
Brian J MacGowan
(Lawrence Livermore National Laboratory, Livermore, CA)
Here we present our findings using physically constrained deep learning models with experimental data, recorded from ICF experiments at the NIF, to predict multiple ICF performance metrics and investigate variability in shot performance. While recent variability studies concentrate on the hybrid-E platform using features that can be difficult to control, we investigate variability using multiple campaigns with well controlled design parameters as features. We highlight features that drive ICF performance and/or variability and try to create a “one shot measurement”, where we map outputs to variability.
This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-ABS-836664
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