Topology optimization via machine learning and deep learning: a review

42Citations
Citations of this article
117Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Topology optimization (TO) is a method of deriving an optimal design that satisfies a given load and boundary conditions within a design domain. This method enables effective design without initial design, but has been limited in use due to high computational costs. At the same time, machine learning (ML) methodology including deep learning has made great progress in the 21st century, and accordingly, many studies have been conducted to enable effective and rapid optimization by applying ML to TO. Therefore, this study reviews and analyzes previous research on ML-based TO (MLTO). Two different perspectives of MLTO are used to review studies: (i) TO and (ii) ML perspectives. The TO perspective addresses "why"to use ML for TO, while the ML perspective addresses "how"to apply ML to TO. In addition, the limitations of current MLTO research and future research directions are examined.

Cite

CITATION STYLE

APA

Shin, S., Shin, D., & Kang, N. (2023, August 1). Topology optimization via machine learning and deep learning: a review. Journal of Computational Design and Engineering. Oxford University Press. https://doi.org/10.1093/jcde/qwad072

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free