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

TL;DR

This paper explores using deep learning to generate a feature space for design exploration, offering a more comprehensive and intuitive alternative to traditional parametric design spaces.

Contribution

It introduces a method to construct and compare feature spaces derived from deep learning with traditional parametric spaces for design exploration.

Findings

Feature space captures complex design relationships.

Parametric space is limited to parameter combinations.

Deep learning-based features enable intuitive visualization.

Abstract

This paper compares the parametric design space with a feature space generated by the extraction of design features using deep learning (DL) as an alternative way for design space exploration. In this comparison, the parametric design space is constructed by creating a synthetic dataset of 15.000 elements using a parametric algorithm and reducing its dimensions for visualization. The feature space - reduced-dimensionality vector space of embedded data features - is constructed by training a DL model on the same dataset. We analyze and compare the extracted design features by reducing their dimension and visualizing the results. We demonstrate that parametric design space is narrow in how it describes the design solutions because it is based on the combination of individual parameters. In comparison, we observed that the feature design space can intuitively represent design solutions according to complex parameter relationships. Based on our results, we discuss the potential of translating the features learned by DL models to provide a mechanism for intuitive design exploration space and visualization of possible design solutions.