Inertial confinement fusion experiments rely on complex multi-physics simulations to guide design work. However, these simulations can be expensive and have several dozen design parameters, making the search for a tuned design difficult. Recently developed automated tools utilize Bayesian optimization to search these high-dimensional spaces for designs. In this project, we use these tools to tune ICF simulations to experimental measurements of a well-characterized shot, N210808, the first MJ yield shot at the National Ignition Facility. This optimization algorithm runs 2D integrated simulations in HYDRA to converge on a design. The algorithm quickly and autonomously adjusts simulation parameters to match measurements such as hotspot shape and bang time. Building on this work, we explore the tool’s ability to search for designs with certain target characteristics. This design search capability can be used to produce designs that match specified scalar values or time series profiles.
LLNL-ABS-2004613