Course: MAA01 25/26 Computational Design III (Level 2) Seminar

Introduction to Optimization and Design Decision-making

Computational Design III (Level 2) advances data-driven design methodologies by integrating optimization, data analysis, and machine learning into architectural decision-making processes. Building on parametric workflows, the seminar explores how environmental simulation tools can inform iterative design through measurable performance metrics. Students engage with both single-objective and multi-objective optimization techniques to generate and navigate complex solution spaces. Expanding on this, the course introduces clustering methods and regression analysis to identify patterns and relationships within large datasets, supporting deeper insights into design performance. Through the application of multi-attribute decision-making techniques, participants develop transparent and reproducible strategies for evaluating and ranking design alternatives within complex architectural contexts.