From optimization to performance-informed design

This paper introduces performance-informed design space exploration (DSE) to question the relationship between explicit, quantitative optimization problems and “wicked”, co-evolving architectural design problems and to support the reframing of architectural design optimization as a medium for reflection. The paper proposes selection, refinement, and understanding as key aspects of performance-informed DSE and surveys current approaches to performance-informed DSE: (1) Clustering and Pareto-based optimization support selection by reducing large numbers of parametric design candidates into smaller and more meaningful sets of choices. (2) Surrogate modelling supports refinement by approximating time-intensive simulations in real-time, which is important for interactivity. (3) Multi-variate visualizations and statistical analyses support understanding by providing insights into characteristics of design spaces and fitness landscapes. Finally, the paper discusses a novel tool for visual and interactive, performance-informed DSE, Performance Explorer. Performance Explorer combines the real-time feedback afforded by surrogate models with a multi-variate visualization of fitness landscapes. A user test of Performance Explorer uncovered several performanceinformed DSE strategies followed by the participants. Consisting of different combinations of selection, refinement, and understanding, these strategies illustrate and—to some extent—validate the proposed framework for performance-informed DSE.