  Reliable plasma transport modeling for magnetic confinement fusion depends on
accurately resolving the ion charge state distribution and radiative power
losses of the plasma. These quantities can be obtained from solutions of a
collisional-radiative (CR) model at each time step within a plasma transport
simulation. However, even compact, approximate CR models can be computationally
onerous to evaluate, and in-situ evaluations of these models within a coupled
plasma transport code can lead to a rigid bottleneck. A way to bypass this
bottleneck is to deploy artificial neural network surrogates for rapid
evaluations of the necessary plasma quantities. However, one issue with
training an accurate artificial neural network surrogate is the reliance on a
sufficiently large and representative data set for both training and
validation, which can be time-consuming to generate. In this study we further
explore a data-driven active learning and training scheme to allow autonomous
adaptive sampling of the problem parameter space that ensures a sufficiently
large and meaningful set of training data assembled for the surrogate training.
Our numerical experiments show that in order to produce a comparably accurate
CR surrogate, the proposed approach requires a total number of data samples
that is an order-of-magnitude smaller than a conventional approach.
