Data-driven topology design using a deep generative model

Shintaro Yamasaki*, Kentaro Yaji, Kikuo Fujita

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

In this paper, we propose a sensitivity-free and multi-objective structural design methodology called data-driven topology design. It is schemed to obtain high-performance material distributions from initially given material distributions in a given design domain. Its basic idea is to iterate the following processes: (i) selecting material distributions from a dataset of material distributions according to eliteness, (ii) generating new material distributions using a deep generative model trained with the selected elite material distributions, and (iii) merging the generated material distributions with the dataset. Because of the nature of a deep generative model, the generated material distributions are diverse and inherit features of the training data, that is, the elite material distributions. Therefore, it is expected that some of the generated material distributions are superior to the current elite material distributions, and by merging the generated material distributions with the dataset, the performances of the newly selected elite material distributions are improved. The performances are further improved by iterating the above processes. The usefulness of data-driven topology design is demonstrated through numerical examples.

Original languageEnglish
Pages (from-to)1401-1420
Number of pages20
JournalStructural and Multidisciplinary Optimization
Volume64
Issue number3
DOIs
Publication statusPublished - 2021 Sep
Externally publishedYes

Keywords

  • Data-driven design
  • Deep generative model
  • Estimation of distribution algorithm
  • Multi-objective methodology
  • Sensitivity-free methodology
  • Topology optimization

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Control and Optimization

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