  Machine learning has recently been applied and deployed at several light
source facilities in the domain of Accelerator Physics. We introduce an
approach based on machine learning to produce a fast-executing model that
predicts the polarization and energy of the radiated light produced at an
insertion device. This paper demonstrates how a machine learning model can be
trained on simulated data and later calibrated to a smaller, limited measured
data set, a technique referred to as transfer learning. This result will enable
users to efficiently determine the insertion device settings for achieving
arbitrary beam characteristics.
