The MSE loss versus the black fraction, 𝑏, averaged across all 15 realizations and mixing steps (𝑁 ) for different training datasets in Fig. indicates very little difference in loss for the four different NN training datasets across the 10 000 evenly spaced validation conditions. Given the same computational effort, the effectiveness of training appears to be insensitive to the number of initial conditions used to span the 0 < 𝑏 < 1 range in the training dataset. The small variability between the different training datasets that is present is well within the bounds of a single geometric standard deviation, the value of which is also consistent across different training sets. Thus, all training datasets tested, regardless of the size and independent of whether the training data is evenly or randomly spaced, can replicate the training data and interpolate between training data configurations.