The NNs used here are, on average, able to replicate the LS method for the training data with good accuracy (MSE loss ∼ 10 -10 ) regardless of the density of the training dataset. The NNs are also able to interpolate to other configurations similar to those used in the training that also begin with single black-white initial conditions, again with good accuracy (MSE loss ∼ 10 -10 ) (Fig. ). Although error in interpolation is for configurations that have small segment lengths (𝑏 ∼ 0, 1), even at these extremes MSE loss is smaller than 10 -7 at the worst (Fig. ). The error in interpolation is variable between different randomly trained realizations of the NN architecture using the same training data, but even the worst cases work reasonably well (MSE loss ∼ 10 -9 ) (Fig. ).