  Transfer learning is a promising approach to creating predictive models that
incorporate simulation and experimental data into a common framework. In this
technique, a neural network is first trained on a large database of
simulations, then partially retrained on sparse sets of experimental data to
adjust predictions to be more consistent with reality. Previously, this
technique has been used to create predictive models of Omega and NIF inertial
confinement fusion (ICF) experiments that are more accurate than simulations
alone. In this work, we conduct a transfer learning driven hypothetical ICF
campaign in which the goal is to maximize experimental neutron yield via
Bayesian optimization. The transfer learning model achieves yields within 5% of
the maximum achievable yield in a modest-sized design space in fewer than 20
experiments. Furthermore, we demonstrate that this method is more efficient at
optimizing designs than traditional model calibration techniques commonly
employed in ICF design. Such an approach to ICF design could enable robust
optimization of experimental performance under uncertainty.

Plasma Physics;Machine Learning