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: (1) TO and (2) 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.

翻译：拓扑优化（TO）是一种在设计域内，在满足给定载荷与边界条件的前提下，推导最优设计的方法。该方法无需初始设计即可实现有效设计，但由于计算成本高昂，其应用一直受到限制。与此同时，包括深度学习在内的机器学习（ML）方法在21世纪取得了巨大进展，因此，许多研究致力于将机器学习应用于拓扑优化，以实现高效且快速的优化。据此，本文对基于机器学习的拓扑优化（MLTO）的相关前期研究进行了回顾与分析。本文从两种不同视角审视了相关研究：（1）拓扑优化视角和（2）机器学习视角。拓扑优化视角探讨“为何”使用机器学习进行拓扑优化，而机器学习视角则关注“如何”将机器学习应用于拓扑优化。此外，本文还审视了当前MLTO研究的局限性以及未来的研究方向。