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Architectural Synthesization

Machine Learning Applications

Course: J-term Exquisite Corpse 2.0 - Machine Learning Applications in Architectural Design
Instructor: Dongyun Kim, George Guida
Date: Jan., 2022

The applications of machine learning to architectural design will begin with an understanding of the benefits and limitations of this technology including bias, intelligence, and creativity. This will then be followed by a series of hands-on workshops covering 2D Style Transfer to 3D object manipulation.

This course will be divided into three parts: Dataset collection, GAN Training, and 3D object manipulations. Dataset collection will equip students with an understanding of the emerging agency of designers and the implicit bias ingrained within these. The training of state-of-the-art machine learning models and their manipulation into new 3D forms will be used to challenge an emerging homogeneity in architectural design. These will additionally shed light on the emerging role of the designer and how images can be synthesized into 3D ‘exquisite corpses’.

KEYWORDS:
Machine Learning, Architectural Design, Style Transfer, GAN, Object Manipulation, Image Synthesization

OBJECTIVES

The tangible skills offered in this workshop will cover the scraping of online datasets, using Python with libraries such as BeautifulSoup, the training of GAN (Generative Adversarial Network) models such as Style Transfer and StyleGAN, and their manipulation through grasshopper workflows. Students can openly extend project topics across architectural, landscape, and urban design interests.