It may be possible to improve mixing for conditions like those in Figs. 14 and 15 by including rotations of LScreated conditions in the training data. However, this is beyond the scope of this study. Nevertheless, it is evident that the NN learns the pattern rather than the method. Attempts to train the NN on completely random inputs (segment endpoints sampled uniformly) have been unsuccessful within the framework laid out in this paper. Additionally, a totally random training dataset undersamples configurations that are expected in the normal progression of mixing while over-sampling configurations that never appear in the desired mixing process.