Data-driven topology design using a deep generative model

Shintaro Yamasaki*, Kentaro Yaji, Kikuo Fujita

*この研究の対応する著者

研究成果: Article査読

1 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)1401-1420
ページ数20
ジャーナルStructural and Multidisciplinary Optimization
64
3
DOI
出版ステータスPublished - 2021 9月
外部発表はい

ASJC Scopus subject areas

  • 制御およびシステム工学
  • ソフトウェア
  • コンピュータ サイエンスの応用
  • コンピュータ グラフィックスおよびコンピュータ支援設計
  • 制御と最適化

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