By training ARRAY on the profiles of different clusters within the city it is able to create new clusters that mimic existing aspects of the city. Training these simulations becomes the way in which you make the city.
Generative adversarial networks, through the use of two neural networks contesting one other in a zero-sum game framework (thus “adversarial”) can learn to mimic various distributions of data and are therefore valuable in generating test dataset’s when these are not readily available.
Once these profiles have been synthesised by Array, the reverse process can take place, with the data being decompressed from profiles to create entirely new city models which retain the characteristics, behaviours and functions of the original, simulating environments, flows and and new urban configurations. These simulated spaces can then be used within
a second layer of simulation.