Computational Explorations

Optimization and machine learning play crucial roles in exploring design spaces, serving as foundational elements in many of today’s scientific advancements. But can we anticipate artificial intelligence replacing human designers in the near future? Computational Explorations invites students to engage with these advanced computational techniques, focusing on their application within architectural design. Students will learn how to use optimization and machine learning to automate the search for optimal design solutions, analyze the resulting data, and make informed predictions, thereby expanding their capacity to explore vast design spaces efficiently. In addition to developing practical skills in data science, students will gain a deeper understanding of these methods' impact on the architectural profession. The module primarily concentrates on performance-based architectural design using building simulations but also encourages students to explore other potential applications. The evaluation consists of weekly assignments completed individually and a collaborative semester project, to be completed in groups of 2 to 3. Prior knowledge of Rhino/Grasshopper and Python programming (as covered in Computational Design) is assumed.

The course is held in person. Non-ITECH Master students will have to complete a coding skill assessment to participate. The seminar will contain weekly individual mini-assignments and a semester-long group assignment.