Designing with data: moving beyond the design space catalogNathan Brown and Caitlin Mueller, ACADIA, 2017
Design space catalogs, which present a collection of different options for selection by human designers, have become commonplace in architecture. Increasingly, these catalogs are rapidly generated using parametric models and informed by simulations that describe energy usage, structural efficiency, daylight availability, views, acoustic properties, and other aspects of building performance. However, by conceiving of computational methods as a means for fostering interactive, collaborative, guided, expert-dependent design processes, many opportunities remain to improve upon the originally static archetype of the design space catalog. This paper presents developments in the areas of interaction, automation, simplification, and visualization that seek to improve on the current catalog model, while also describing a vision for effective computer-aided, performance-based design processes in the future.
Quantifying diversity in computational designResearch, 2015 - Present
To be useful for architects searching for creative, expressive forms, multi-objective optimization tools must generate a diverse range of design solutions. This gives the designer flexibility to choose from a number of high-performing designs based on aesthetic preferences or specific performance priorities. However, there is no single established method for measuring diversity, and no explicit understanding of how greater optimization output diversity leads to better architectural outcomes. This research project explores different metrics for quantifying diversity and tests how users interact with design processes that employ various diversity measurements.