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This paper compares the parametric design space with a feature spacegenerated by the extraction of design features using deep learning (DL) as analternative way for design space exploration. In this comparison, theparametric design space is constructed by creating a synthetic dataset of15.000 elements using a parametric algorithm and reducing its dimensions forvisualization. The feature space - reduced-dimensionality vector space ofembedded data features - is constructed by training a DL model on the samedataset. We analyze and compare the extracted design features by reducing theirdimension and visualizing the results. We demonstrate that parametric designspace is narrow in how it describes the design solutions because it is based onthe combination of individual parameters. In comparison, we observed that thefeature design space can intuitively represent design solutions according tocomplex parameter relationships. Based on our results, we discuss the potentialof translating the features learned by DL models to provide a mechanism forintuitive design exploration space and visualization of possible designsolutions.

Feature space exploration as an alternative for design space exploration beyond the parametric space