Data-driven models for improved preshot predictions of ICF experiments at the NIF

Standard computer simulations for indirect drive inertial confinement fusion (ICF), without platform-specific corrections, often show discrepancy with experiments. In this talk, we present a machine learning based method for training models that correct for this discrepancy, and are thus more predictive of National Ignition Facility (NIF) ICF experiments than simulations alone.

We combine simulation and experimental data via a technique called “transfer learning” to produce a model that is predictive of NIF experiments from a wide variety of campaigns, and becomes more accurate as more experimental data are acquired. This model has been used to predict the outcome of recent DT experiments at the NIF with progressively increasing accuracy.

This data-driven model can play a valuable role in future design exploration by providing empirically realistic sensitivities to design parameters. We illustrate how transfer learned corrections to simulation predictions could guide us toward high performing designs more efficiently than simulations alone.