The architecture of the NN is specified by hyperparameters. For this study, the hyperparameters include the number of layers, the number of neurons per layer, the activation function, and the initial optimizer learning rate . The choices for these hyperparameters are somewhat arbitrary, and good results can be had across wide ranges of hyperparameter values. Here, the hyperparameters are chosen using Tune , a tool for testing a sample of randomly generated NN architectures for performance on a subset of the data not used in training called the validation set. To determine the hyperparameters, we produce a dataset of 10 000 random (with respect to 𝑏) single black-white initial conditions and all 31 of each initial condition's LS-generated descendant conditions to train randomly generated NNs with different hyperparameters. We evaluate their performance in terms of MSE loss on a separate validation set consisting of 1 000 other randomly created single black-white initial conditions and their LS descendants. The hyperparameters of the NN that best transfer its performance to the validation set define the architecture of the NN used in this paper. The NNs considered in Sec. IV, which use different training and validation sets, inherit the architecture chosen during the hyperparameter optimization process.