This project, undertaken as part of my master’s program, explores the integration of advanced technologies, including machine learning, deep neural networks, data analytics, and parametric architecture. The focus is on generating synthetic datasets to model urban architectural forms, which are subsequently analyzed and simulated. These evaluations inform the training of machine learning models to identify and optimize high-performing spatial configurations.

The final outcome leverages a pre-trained model to propose optimized urban layouts based on user-defined parameters, such as the number of buildings, population size, and desired floor area. These configurations are automatically generated, stored, and visualized as Industry Foundation Classes (IFC) files, ensuring compatibility with architectural and urban planning workflows.

TECHNIQUES : BLENDER | SVERCHOK | RADIANCE | RANDOM FOREST | PYTHON