GAN models are susceptible to "mode collapse" which means that they generate samples only from a subset of the expected categories. This also occurs for the models described in this study, where a single model generates only one or a few of the possible relevant configurations of the system. We therefore always train multiple models with different starting weights and hyperparameters, and incorporate predictions from all of these. Even if our models experience mode collapse, they can collapse into different basins, thus the use of several models results in a much better sampling overall.