Creative AI Ecologies | Augmenting Architectural Agency

Research project and a workshop, led by: Daniel Bolojan, Shermeen Yousif, Emmanouil Vermisso In light of the observed integration of Artificial Intelligence within many industries, this workshop reconsiders the “Architectural Design Cycle”, proposing nested generative AI design processes. Rather than thinking about AI as a “closed” cycle of “input-output”, a series of complementary deep neuralContinue reading “Creative AI Ecologies | Augmenting Architectural Agency”

Shape Clustering Using K-Medoids in Architectural Form Finding

Research Title: Shape Clustering Using K-Medoids in Architectural Form Finding Abstract: As the number of design candidates in generative systems is often high, there is a need for an articulation mechanism that assists designers in exploring the generated design set. This research aims to condense the solution set yet enhance heterogeneity in generative design systems.Continue reading “Shape Clustering Using K-Medoids in Architectural Form Finding”

Incorporating Form Diversity into Architectural Design Optimization

Research Title: Incorporating Form Diversity into Architectural Design Optimization Abstract: In this study, we introduce a new approach that incorporates form diversity into architectural design optimization, which will potentially accommodate designers’ aesthetic judgment into the whole building optimization process. Form diversity is defined here as the level of difference in building geometric forms. We developedContinue reading “Incorporating Form Diversity into Architectural Design Optimization”

Machine Learning for an Articulated Design Space

Research Title: Application of an Automatic Shape Clustering Method into Generative and Design Optimization Systems Abstract: Despite their prevalence and extensive applications, generative and design optimization systems lack effective organizational methods of the excessive number of design options they produce, which is problematic for designers’ interaction. Ideally, a diverse and organized set of designs canContinue reading “Machine Learning for an Articulated Design Space”


Deep-Performance: Incorporating Deep Learning for Automating Building Performance Simulation in Generative Systems Abstract: In this study, we introduce a newly developed method called Deep-Performance, to enable automatic environmental performance simulation prediction without the need to perform simulations, by integrating deep learning strategies. The aim is to train neural networks on datasets with thousands of buildingContinue reading “Deep-Performance”