This workshop offered at the Design Modeling Symposium in Berlin presents tools and techniques for the application of Machine Learning (ML) to Generative Architectural Design (GAD).

Proposed here is a modest modification of a 3-step process that is well-known in generative architectural design, and that proceeds as: generate, evaluate, iterate. In place of the typical approaches to the evaluation step of this cycle, we have developed techniques to employ an ML process: a Convolutional Neural Net (CNN) trained to perform image classification. Such an approach holds significant ramifications for the overall design model, as it allows the integration of a variety of tacit and heretofore un-encapsulatable design criteria - such as architectural style, spatial experience, or typological features - into existing generative design workflows.

While existing research1 has integrated low-level ML operations into the parametric design environment with a level of success, this proposal uniquely links the familiar environment of Grasshopper, which facilitates the general generative design cycle, with cloud-hosted ML models.

In this, we employ two high-level frameworks, Lobe.ai and Ludwig (both based on the popular Tensorflow framework), that facilitate the training of CNNs with little or no scripting required.

Extending work completed at the Smart Geometry Workshop in 2018 in Toronto, this workshop directly supports two of the stated aims of the 2019 Design modeling Symposium, as it proposes specific methods for designing with high-complexity and AI-based models in the interest of integrating social cultural and aesthetic criteria into existing processes of design.