ABSTRACT
Exposing architectural concepts to modern AI/ML methods requires succinct representations that can capture the essential relationship between adjacent architectural elements in design samples. We explore various examples of topological spaces—from rudimentary graphs to richer closure-finite weak topology (CW)-complexes—and how they can be used to model architectural design samples. We then argue how AI concepts developed for graphs can be naturally extended to more general topological spaces with the following goals in mind: (1) finding salient building-block substructures that occur consistently in highly rated designs; (2) unearthing latent design rules from data by characterizing the space of designs using graph representation learning followed by topological dimensionality reduction; and (3) generating novel designs—given training design samples—using generative adversarial networks applied to graphs. Our preliminary results on the application of AI/ML on graphs are based on the architectural design data from Arcbazar, an online crowdsourcing platform for architectural designs.
