Inference of three-dimensional hot-spot and shell morphology in inertial confinement fusion experiments using a convolutional neural network

The performance of inertial confinement fusion (ICF) implosions is sensitive to the three-dimensional (3D) morphology of the hot-spot and shell configurations. The ability to infer shell-mass uniformity and reconstruct 3D hot spots is crucial for quantifying the degradation of ignition criteria and improving symmetry in ICF implosion experiments. In this work, we present a deep-learning convolutional neural network (CNN) for reconstructing 3D hot-spot and shell structures for ICF capsules. The 3D geometry of the hot spot is reconstructed from x-ray images measured from multiple lines of sight on OMEGA. The shell configuration is inferred indirectly through machine learning using a convolutional neural network extensively trained on a dec3d simulation database. This simulation-dependent approach yields consistent agreement between reconstructed 3D shell densities and machine-learning optimized dec3d simulation results. This work demonstrates a CNN framework that successfully reconstructs 3D capsule structures from two-dimensional images in ICF implosions.. Authors: Woo, K. M. [Laboratory for Laser Energetics, Rochester, NY (United States)] (ORCID:0000000211243871); Buck, J. [University of Rochester, NY (United States)] (ORCID:0000000178043308); Churnetski, K. [Laboratory for Laser Energetics, Rochester, NY (United States)]; Betti, R. [Laboratory for Laser Energetics, Rochester, NY (United States)]; Thomas, C. A. [Laboratory for Laser Energetics, Rochester, NY (United States)]. DOE Contract: NA0004144; SC0024381; SC0024456. Subjects: 70 PLASMA PHYSICS AND FUSION TECHNOLOGY; Direct drive; Implosion symmetry; Inertial confinement fusion; Nuclear fusion; Plasma fusion; Plasma sources