AI in Design: A Conversation With Josef Musil on the Use of Machine Learning in Architecture Design
During his time in Foster + Partners he has worked on projects ranging from the facade screen for the Edmond & Lily Safra Center for Brain Sciences at the University of Jerusalem, where he built a generative model for small section of the brain along with an automated workflow that reads data form 3D neuroscientific scans and implemented structural analysis results and manufacturing constraints, to developing a working functional prototype of a small number of 3D printing swarm-based robots for a NASA organized Mars habitat competition. Currently he is looking at implementations of machine learning algorithms into architectural design process and simulation.
One area that is prime for the use of machine learning algorithms in design is in the development of surrogate models that can be used in early design stages to quickly iterate through different design options and get quick feedback about its performance. A surrogate model is a simpler model of a slower computational process (such as a physics-based simulation) and is usually developed to learn nonlinear relationships between input and output values of the original model. Musil has been working on developing these sorts of surrogate models to enable interactive fast design cycles and reverse typical design workflows. We spoke with Musil about how he uses Machine Learning as part of the design process and how surrogate models can help better understand different design constraints.