“…For example, dynamical vacuum selection [8] on a network of string geometries [9] in a well-studied bubble cosmology [10] selects models with large numbers of gauge groups and axions, as well as strong coupling. However, concrete studies of the landscape are difficult due to its enormity [11,12,13,14,9,15], computational complexity [16,17,18,19,20], and undecidability [17]. It is therefore natural to expect that, in addition to the formal progress that is clearly required, data science techniques such as supervised machine learning will be necessary to understand the landscape; see for initial works [21,22,8,23] in this directions and [24,25,26,27,28,29,30] for additional promising results.…”