Advancing graph-supported machine learning in generative design for architectural engineering

Abstract

This review maps how graph-supported machine learning is used in generative design for architectural engineering. It examines data, modelling approaches, and practical limitations, and identifies opportunities for combining graph models with physical knowledge and designer feedback.

Cai Wu

Assistant Professor

I study computational urban morphology, GeoAI, and data-driven urban design, developing reproducible methods for understanding cities across scales.