We describe our work on providing support for design decision making in generative design systems producing large quantities of data, motivated by the continuing challenge of making sense of large design and simulation result datasets. Our approach provides methods and tools for
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We describe our work on providing support for design decision making in generative design systems producing large quantities of data, motivated by the continuing challenge of making sense of large design and simulation result datasets. Our approach provides methods and tools for multivariate interactive data visualization of the generated designs and simulation results, enabling designers to focus not only on high-performing results but also examine suboptimal designs’ attributes and outcomes, thus discovering relationships giving greater insight to design performance and facilitating guidance of further design generation. We illustrate this by an example exploring building massing and envelope design (fenestration arrangement and external shading) with simulations of daylighting and heat gain. We conclude that the visualization techniques investigated can help designers better comprehend inter-relationships between variable parameters, constraints and outcomes, with consequent benefits of: finding good design outcomes; verifying that simulation results are reliable and; understanding characteristics of the fitness landscape.@en
