Linearized neural network variability model of megajoule yield shots at the National Ignition Facility

The inertial confinement fusion (ICF) program at the National Ignition Facility (NIF) achieved a record-breaking 1.3 MJ yield from its N210808 experiment. Efforts have now begun on developing a robust, reproducible platform for delivering MJ-class yields. As part of that effort, several repeat experiments of N210808 were made to assess shot-to-shot variability of the design. In this talk, we analyze the set of repeat shots to characterize the variability under various design perturbations. We do this by combining a neural network surrogate trained on simulated (via HYDRA) capsule implosions with a linearized variability model inferred from the repeat shots. We apply the model to an ensemble of perturbed designs centered on N210808, demonstrating the existence of a high-variability region that needs to be avoided for successful implementation of a robust MJ platform.