Project Discover is a workflow for generative design for architecture. It involves the integration of a rule-based geometric system, a series of measurable goals, and a system for automatically generating, evaluating, and evolving a very large number of design options. The result is a tool to explore a wide design space, and get closer and closer to achieving all of the goals simultaneously.
By starting with high-level goals and constraints and then using the power of computation to optimize for multiple criteria, Project Discover produces high-performing and novel designs that would not have been possible to create without this approach.
Lorenzo Villaggi, James Stoddart, Pan Zhang, Alex Tessier, David Benjamin (2019)Design Loop: Calibration of a Simulation of Productive Congestion Through Real-World Data for Generative Design Frameworks
Danil Nagy, Lorenzo Villaggi, David Benjamin (2018)Generative Urban Design: Integration of financial and energy design goals in a generative design workflow for residential neighborhood layout
Danil Nagy, Lorenzo Villaggi, James Stoddart, David Benjamin (2017)The Buzz Metric: A Graph-based Method for Quantifying Productive Congestion in Generative Space Planning for Architecture
Danil Nagy, Lorenzo Villaggi, David Benjamin (2017)Beyond heuristics: A novel design space model for generative space planning in architecture
Lorenzo Villaggi, James Stoddart, Danil Nagy, David Benjamin (2017)Survey-Based Simulation of User Satisfaction for Generative Design in Architecture
Justin Berquist, Alex Tessier, Azam Khan, William O’Brien, Ramtin Attar (2017)An Investigation of Generative Design for Heating,Ventilation, and Air-Conditioning
Danil Nagy, Damon Lau , John Locke, James Stoddart, Lorenzo Villaggi, Ray Wang, Dale Zhao, David Benjamin (2017)Project Discover: An application of generative design for architectural space planning