The fraction of configurations that maintain consistent interface generation as the 15 different realizations of NN 1k described in Sec. IV C mixes them is plotted in Fig. ) versus 𝑁 (gray) and their average (black) compared to random mixing (red). As 𝑁 increases, the error in cut locations resulting from the shrinking segment sizes causes more configurations to not mix effectively or at all. Some realizations perform significantly worse than the average and three in particular only mix about 60% of the initial conditions to the optimal number of interfaces, although this is still far better than random mixing. In fact, there are drop-offs in interface generation at optimally mixed states (with respect to the LS method) corresponding to 𝑁 = 7, 15 and 31. When the LS method arrives at an optimally mixed state or the NN arrives at a suboptimal approximation of such a state, the largest segments are all finally reduced in length by half or roughly half (depending on the NN accuracy). These smaller segments cause the NN to increasingly fail to mix as the requisite accuracy needed to place cuts within the proper segments increases. Nevertheless, on average, NN 1k consistently extrapolates beyond the single-step horizon for which it has been trained, successfully creating new interfaces for nearly 86% of the validation data initial conditions for every time-horizon to 𝑁 = 31.