The average MSE losses relative to the mixing step 𝑁 for all training datasets are also similar, as shown in Fig. . The combination of the LS method and the NN architecture results in slightly better performance for certain mixing steps, specifically a drop in loss after 𝑁 = 7, 15 and an increase around 𝑁 = 25. The drops in loss after 𝑁 = 7, 15 are most likely related to the optimal mixing states at 𝑁 = 2 𝑖 -1 that have equal length segments of each color. These 𝑁 = 2 𝑖 -1 states and those immediately preceding them are also the states for which the LS method places cut locations closest to the domain boundary as it cuts the leftmost and rightmost segments, respectively, and exhibit the largest error as indicated in Figs. , and 8.