Architecture Engineers Award

The evolution of architectural design is increasingly shaped by computational tools that support complex form exploration and performance-driven decision-making. Traditional form-finding methods are often limited by the designer’s manual iteration speed and the inability of existing machine learning models to generate structurally coherent, user-guided architectural forms. Emerging techniques, such as Stable Diffusion and Low-Rank Adaptation (LoRA), enable more efficient training and controlled generation of 3D morphological structures. By integrating heat-map-based geometry control with diffusion models, architects gain a powerful workflow for producing diverse design alternatives and achieving seamless transitions from conceptual forms to realistic renderings. This research situates itself within the broader framework of AI-assisted design and aims to expand the creative and technical capabilities of form-finding practices. Limitations of Existing Machine Learning Approaches in Form...