Deep Learning Research

Deep-Performance

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 building design samples and their corresponding performance simulation. The trained model would offer performance prediction for design options emerging in generative protocols. The research is a work-in-progress within a broader project aimed at automating buildings’ environmental performance evaluations of daylight analysis and energy simulation, using deep…

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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 neural networks examine the potential of a logical continuity in AI-driven workflows for architecture, simultaneously challenging and augmenting the designer’s agency. The workshop will deploy AI creativity to tackle a variety of architectural systems, including formal articulation, structural logic, and enclosure responsiveness. Combining parametric and AI…

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