AMLD Workshop Generative AI for Forward and Inverse Design in Architecture and Engineering

Traditionally, architectural and engineering design involves combining and optimizing many criteria and constraints. For performance-driven design, architects and engineers create parametric design models to generate, simulate, and evaluate many design instances, to gather performance feedback on design alterations. However, this is typically a challenging process, especially in the case of large design problems, limiting the designers to only investigate a narrow spectrum of possible solutions.

At the SDSC, in collaboration with Gramazio Kohler and the group on Concrete Structures and Bridge Design at ETH Zürich, we have developed the tool "AI-eXtended Design" for generative design for parametric modeling. During this workshop, you will be able to explore its possibilities and understand how to harness it to enhance your design workflows using machine learning.

Prerequisites

• Attendees should bring their laptop, and preferably pre-install the AIXD tools and Python environments required.
• The workshop is best suited for practitioners and scientists with at least intermediate experience in Python. It is aimed at architects and engineers with basic coding skills who want to leverage machine learning in their work, as well as computer/data scientists interested in applying their skills in the architecture or engineering domain.

* Recommended: basic understanding of machine learning, generative AI, experience with Python, Jupyter notebook, Pandas.