Article:

The field of generative architectural design has explored a wide range ofapproaches in the automation of design production, but these approaches havedemonstrated limited artificial intelligence. Generative Adversarial Networks(GANs) are a leading deep generative model that use deep neural networks(DNNs) to learn from a set of training examples in order to create new designinstances with a degree of flexibility and fidelity that outperform competinggenerative approaches. Their application to generative tasks in architecture,however, has been limited. This research contributes new knowledge on the use ofGANs for architectural plan generation and analysis in relation to the work ofspecific architects. Specifically, GANs are trained to synthesize architecturalplans from the work of the architect Le Corbusier and are used to provideanalytic insight. Experiments demonstrate the efficacy of different augmentationtechniques that architects can use when working with small datasets.