  We present a study of using machine learning to enhance hohlraum design for
opacity measurement experiments. For opacity experiments we desire a hohlraum
that, when its interior walls are illuminated by theNational Ignition Facility
(NIF) lasers, will produce a high radiation flux that heats a central sample to
a temperature that is constant over a measurement time window. Given a baseline
hohlraum design and a computational model, we train a deep neural network to
predict the time evolution of the radiation temperature as measured by the
Dante diagnostic. This enables us to rapidly explore design space and determine
the effect of adjusting design parameters. We also construct an "inverse"
machine learning model that predicts the design parameters given a desired time
history of radiation temperature. Calculations using the machine learning model
demonstrate that improved performance over the baseline hohlraum would reduce
uncertainties in experimental opacity measurements.
