From exploration to interpretation: Adopting deep representation learning models to latent space lnterpretation of architectural design alternatives

Abstract

An informative interpretation of the hyper-dimensional design solution space can potentially enhance the cognitive capacity of designers with respect to both conventional design practice and the research domain of computational-aided generative design. However, the hitherto research of design space exploration has had limited focus on the interpretation of the hyper solution space per se due to the knowledge gap pertaining to representation and generation. Representation learning techniques, as a core paradigm in the statistically empowered domain of machine learning, possess the capability of extracting a convoluted probabilistic distribution of hyperspace with latent features from unorganized data sources in a generalized manner, which can be an intuitive modus operandi for a structural interpretation of the intricate latent design solution space and benefit the challenging task of architectural design exploration. We examine and demonstrate the potential capabilities of representation learning techniques for the interpretation of latent architectural design solution space with consideration of disentanglement and diversity.