Despite the overall success of this particular NN 1k , configurations with small amounts of one color (𝑏 ∼ 0, 1) display consistently higher MSE loss even though the training configurations are uniformly sampled with respect to 𝑏 and the MSE loss is computed using the differences in absolute cut locations. Although configurations with small regions of one color require higher precision to place cuts properly, MSE loss is computed separately from segment size, so smaller segments should not result in larger error due to precision discrepancies. It is also possible that cut locations closest to domain boundaries are the least accurate simply because the NN is trained to place cuts within the domain boundaries. In fact, training a NN using only 𝑏 values in the range 0 < 𝑏 < 0.1 reduces the MSE loss in this range to 10 -10 , similar to that for values of 𝑏 not near 0 or 1 trained with 0 < 𝑏 < 1 (Fig. ). Interestingly, when training with 0 < 𝑏 < 0.1 the MSE loss is smallest in the middle of this range (𝑏 = 0.05) and rises near 𝑏 ∼ 0, 0.1. Thus, it appears that the NN is most successful at mimicking the LS method at the center of the training data distribution simply because this is where most training examples occur, whereas training data near the edge of the training distribution is more limited. This is analogous to the error associated with calculating a moving average where the number of values available for the moving average diminishes near the edge of the data distribution, or to the confidence intervals for a regression where error is more likely to be small near the center of the distribution. Although not implemented in the training data here, a periodic boundary for 0 < 𝑏 < 1 training data could eliminate the edges of the training data distribution altogether.