Abstract

This chapter details how machine learning can help in the translation between an architectural element’s description and its making. With a specific focus on digital fabrication and deep learning, the chapter identifies new integrative workflows and datasets enabled by machine learning that can enable architects to rethink critical parameters of design, materiality, and fabrication. Three particular trajectories are identified—new opportunities for making fabrication information, new opportunities for material complexity, and new opportunities for interaction between humans and machines. Two case studies then exemplify the use of machine learning within the digital chain to (A) introduce flexibility and simplicity to the making of fabrication information and (B) capture complex interdependencies between material and fabrication parameters.