  The design space for inertial confinement fusion (ICF) experiments is vast
and experiments are extremely expensive. Researchers rely heavily on computer
simulations to explore the design space in search of high-performing
implosions. However, ICF multiphysics codes must make simplifying assumptions,
and thus deviate from experimental measurements for complex implosions. For
more effective design and investigation, simulations require input from past
experimental data to better predict future performance. In this work, we
describe a cognitive simulation method for combining simulation and
experimental data into a common, predictive model. This method leverages a
machine learning technique called transfer learning, the process of taking a
model trained to solve one task, and partially retraining it on a sparse
dataset to solve a different, but related task. In the context of ICF design,
neural network models trained on large simulation databases and partially
retrained on experimental data, producing models that are far more accurate
than simulations alone. We demonstrate improved model performance for a range
of ICF experiments at the National Ignition Facility, and predict the outcome
of recent experiments with less than ten percent error for several key
observables. We discuss how the methods might be used to carry out a
data-driven experimental campaign to optimize performance, illustrating the key
product -- models that become increasingly accurate as data is acquired.
