Application Of CGANs (Conditional Generative Adversarial Networks) In Architecture

Abstract

This research paper delves into the expansive applications of Conditional Generative Adversarial Networks (cGANs) within the realm of architecture. Moving beyond traditional design methodologies, it investigates how cGANs can be employed to generate diverse architectural solutions that are both innovative and contextually responsive. While a portion of the study does focus on a case study involving generative design based on radiation analysis in Indore city, the scope of the research is much broader. It encompasses a comprehensive exploration of the potential of cGANs in various aspects of architectural design and planning. The paper also includes a demonstration of a generative design model, showcasing the capabilities of cGANs in creating optimized design outcomes. The findings underscore the transformative implications of integrating advanced machine learning techniques in architectural practice, heralding a new era of design that is adaptive, sustainable, and forward-thinking.