  We apply machine learning to the problem of finding numerical Calabi-Yau
metrics. We extend previous work on learning approximate Ricci-flat metrics
calculated using Donaldson's algorithm to the much more accurate "optimal"
metrics of Headrick and Nassar. We show that machine learning is able to
predict the K\"ahler potential of a Calabi-Yau metric having seen only a small
sample of training data.
