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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April 2023
Research Article|
April 14 2023
Investigating boosted decision trees as a guide for inertial confinement fusion design Available to Purchase
Andrew D. Maris
;
Andrew D. Maris
(Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing)
1
Lawrence Livermore National Laboratory
, Livermore, California 94550, USA
2
Department of Nuclear Science and Engineering, Massachusetts Institute of Technology
, Cambridge, Massachusetts 02139, USA
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Shahab F. Khan
;
Shahab F. Khan
a)
(Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Visualization, Writing – original draft, Writing – review & editing)
1
Lawrence Livermore National Laboratory
, Livermore, California 94550, USA
a)Author to whom correspondence should be addressed: [email protected]
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Michael M. Pokornik
;
Michael M. Pokornik
(Formal analysis, Investigation, Software, Visualization)
1
Lawrence Livermore National Laboratory
, Livermore, California 94550, USA
3
Department of Mechanical and Aerospace Engineering, University of California San Diego
, San Diego, California 92093, USA
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J. Luc Peterson
;
J. Luc Peterson
(Methodology, Validation, Writing – review & editing)
1
Lawrence Livermore National Laboratory
, Livermore, California 94550, USA
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Kelli D. Humbird
;
Kelli D. Humbird
(Methodology, Validation, Writing – review & editing)
1
Lawrence Livermore National Laboratory
, Livermore, California 94550, USA
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Steven W. Haan
Steven W. Haan
(Data curation, Writing – review & editing)
1
Lawrence Livermore National Laboratory
, Livermore, California 94550, USA
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Andrew D. Maris
1,2
Shahab F. Khan
1,a)
Michael M. Pokornik
1,3
J. Luc Peterson
1
Kelli D. Humbird
1
Steven W. Haan
1
1
Lawrence Livermore National Laboratory
, Livermore, California 94550, USA
2
Department of Nuclear Science and Engineering, Massachusetts Institute of Technology
, Cambridge, Massachusetts 02139, USA
3
Department of Mechanical and Aerospace Engineering, University of California San Diego
, San Diego, California 92093, USA
a)Author to whom correspondence should be addressed: [email protected]
Phys. Plasmas 30, 042713 (2023)
Article history
Received:
July 19 2022
Accepted:
March 25 2023
Citation
Andrew D. Maris, Shahab F. Khan, Michael M. Pokornik, J. Luc Peterson, Kelli D. Humbird, Steven W. Haan; Investigating boosted decision trees as a guide for inertial confinement fusion design. Phys. Plasmas 1 April 2023; 30 (4): 042713. https://doi.org/10.1063/5.0111627
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