  Most design methods contain a forward framework, asking for primary
specifications of a building to generate an output or assess its performance.
However, architects urge for specific objectives though uncertain of the proper
design parameters. Deep Learning (DL) algorithms provide an intelligent
workflow in which the system can learn from sequential training experiments.
This study applies a method using DL algorithms towards generating demanded
design options. In this study, an object recognition problem is investigated to
initially predict the label of unseen sample images based on training dataset
consisting of different types of synthetic 2D shapes; later, a generative DL
algorithm is applied to be trained and generate new shapes for given labels. In
the next step, the algorithm is trained to generate a window/wall pattern for
desired light/shadow performance based on the spatial daylight autonomy (sDA)
metrics. The experiments show promising results both in predicting unseen
sample shapes and generating new design options.
