“…Generative networks open new possibilities to enhance the efficiency of simulations [1,6]. They are able to learn underlying distributions with high precision [7,8,9,10,11,12,13,14] and can therefore provide more efficient phase space mapping [15,16,17,18,19,20], amplify [21,22] and compress data [23], serve as surrogate models in phenomenological studies and provide fast detector simulations [24,25,26]. Finally generative networks enable the inversion of the simulation chain [27,28,29].…”