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Over 120 DT ice layer thermonuclear (TN) ignition experiments in inertial confinement fusion (ICF) were conducted on the National Ignition Facility (NIF) in the last eight years. None of the experiments achieved ignition. In fact, the measured neutron outputs from the experiments were well below what was expected. Although experiments to fine-tune the target designs are the focus of the national ICF program, insightful analysis of the existing data is a pressing need. In highly integrated ignition experiments, it is impossible to vary only one design parameter without perturbing all the other implosion variables. Thus, to determine the nonlinear relationships between the design parameters and performance from the data, a multivariate analysis based on physics models is necessary. To this end, we apply machine learning and deep learning methods to the existing NIF experimental data to uncover the patterns and physics scaling laws in TN ignition. In this study, we focus on the scaling laws between the implosion parameters and neutron yield using different supervised learning methods. Descriptions, comparisons, and contrasts between the methods are presented. Our results show that these models are able to infer a relationship between the observed stagnation conditions and neutron yields. This exploratory study will help build new capabilities to evaluate capsule designs and provide suggestions for new designs.

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We explore the applications of a variety of machine learning techniques in relativistic laser-plasma experiments beyond optimization purposes. With the trained supervised learning models, the beam charge of electrons produced in a laser wakefield accelerator is predicted given the laser wavefront change caused by a deformable mirror. Feature importance analysis using the trained models shows that specific aberrations in the laser wavefront are favored in generating higher beam charges, which reveals more information than the genetic algorithms and the statistical correlation do. The predictive models enable operations beyond merely searching for an optimal beam charge. The quality of the measured data is characterized, and anomaly detection is demonstrated. The model robustness against measurement errors is examined by applying a range of virtual measurement error bars to the experimental data. This work demonstrates a route to machine learning applications in a highly nonlinear problem of relativistic laser-plasma interaction for in-depth data analysis to assist physics interpretation.