Dr. Imdat As published "Artificial intelligence in architecture: Generating conceptual design via deep learning," co-authored with Prithwish Basu and Siddarth Pal from Raytheon BBN Corp., in the quarterly International Journal of Architectural Computing (IJAC). This research has been funded by DARPA (the US Defense Advanced Research Projects Agency).
Artificial intelligence, and in particular machine learning, is a fast-emerging field. Research on artificial intelligence focuses mainly on image-, text- and voice-based applications, leading to breakthrough developments in self-driving cars, voice recognition algorithms and recommendation systems. In this article, Dr. As and his colleagues present the research of an alternative graph-based machine learning system that deals with three-dimensional space, which is more structured and combinatorial than images, text or voice. Specifically, a function-driven deep learning approach to generate conceptual design. They trained and used deep neural networks to evaluate existing designs encoded as graphs, extract significant building blocks as subgraphs and merge them into new compositions. Finally, the work explored the application of generative adversarial networks to generate entirely new and unique designs.
Read full journal article at https://journals.sagepub.com/doi/abs/10.1177/1478077118800982?journalCode=jaca