Design subspace learning: Structural design space exploration using performance-conditioned generative modeling

Danhaive, Renaud

doi:10.1016/j.autcon.2021.103664

Return

Design subspace learning: Structural design space exploration using performance-conditioned generative modeling

2021-07-01

PRE

Danhaive, Renaud *

Mueller, Caitlin T.

DOI：10.1016/j.autcon.2021.103664

Original

Original request for help

Save

Abstract

En 中文

Designers increasingly rely on parametric design studies to explore and improve structural concepts based on quantifiable metrics, generally either by generating design variations manually or using optimization methods. Unfortunately, both of these approaches have important shortcomings: effectively searching a large design space manually is infeasible, and design optimization overlooks qualitative aspects important in architectural and structural design. There is a need for methods that take advantage of computing intelligence to augment a designer's creativity while guiding-not forcing-their search for better-performing solutions. This research addresses this need by integrating conditional variational autoencoders in a performance-driven design exploration framework. First, a sampling algorithm generates a dataset of meaningful design options from an unwieldy design space. Second, a performance-conditioned variational autoencoder with a low-dimensional latent space is trained using the collected data. This latent space is intuitive to explore by designers even as it offers a diversity of high-performing design options.

Keywords:

Deep generative modeling

Latent space

Latent variable

Variational autoencoder

Design space

Computational design

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.