Artificial intelligence in architecture: Generating conceptual design via deep learning
Abstract
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, we 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, we present a function-driven deep learning approach to generate conceptual design. We 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, we explored the application of generative adversarial networks to generate entirely new and unique designs.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 28, 2018
- Source ID
- 10.1177/1478077118800982
Entities
People
- Imdat As
- Prithwish Basu
- Siddharth Pal
Organizations
- Defense Advanced Research Projects Agency
- RTX
- University of Hartford