Broadly, NNs are continuous function approximators, and the training process fits the function approximation, much like a regression, to the training data. Since the training datasets are not exhaustive of all possible inputs, let alone all inputs of the same type (single black-white initial conditions and LS descendants), we are interested in the generalization of the NN to configurations outside of the training set. The most basic check of this is to evaluate performance on data from the same distribution as the training data, namely LS method cut locations from single black-white initial conditions and their LS descendants. We refer to the ability of NNs to reproduce cuts consistent with the LS method for data created in the same way as the training data as "replication" for values of 𝑏 identical to the training set and "interpolation" for values of 𝑏 between training dataset values of 𝑏.