Investigating boosted decision trees as a guide for inertial confinement fusion design

Maris, Andrew D.; Khan, Shahab F.; Pokornik, Michael M.; Peterson, J. Luc; Humbird, Kelli D.; Haan, Steven W.

doi:10.1063/5.0111627

Title: Investigating boosted decision trees as a guide for inertial confinement fusion design

Journal Article · Fri Apr 14 00:00:00 EDT 2023 · Physics of Plasmas

DOI:https://doi.org/10.1063/5.0111627· OSTI ID:1971305

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Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Massachusetts Institute of Technology (MIT), Cambridge, MA (United States)
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Inertial confined fusion experiments at the National Ignition Facility have recently entered a new regime approaching ignition. Improved modeling and exploration of the experimental parameter space were essential to deepening our understanding of the mechanisms that degrade and amplify the neutron yield. The growing prevalence of machine learning in fusion studies opens a new avenue for investigation. In this paper, we have applied the Gradient-Boosted Decision Tree machine-learning architecture to further explore the parameter space and find correlations with the neutron yield, a key performance indicator. We find reasonable agreement between the measured and predicted yield, with a mean absolute percentage error on a randomly assigned test set of 35.5%. This model finds the characteristics of the laser pulse to be the most influential in prediction, as well as the hohlraum laser entrance hole diameter and an enhanced capsule fabrication technique. We used the trained model to scan over the design space of experiments from three different campaigns to evaluate the potential of this technique to provide design changes that could improve the resulting neutron yield. While these data-driven model cannot predict ignition without examples of ignited shots in the training set, it can be used to indicate that an unseen shot design will at least be in the upper range of previously observed neutron yields.

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Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 1971305

Alternate ID(s):: OSTI ID: 1970067

Report Number(s):: LLNL-JRNL-830845; 1047133; TRN: US2313559

Journal Information:: Physics of Plasmas, Vol. 30, Issue 4; ISSN 1070-664X

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

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