Topology Optimization via Machine Learning and Deep Learning: A Review
It provides a comprehensive overview for researchers in computational design and engineering, but is incremental as it synthesizes existing work rather than presenting new methods.
This paper reviews research on applying machine learning and deep learning to topology optimization, analyzing studies from both optimization and machine learning perspectives to address computational costs and enable faster design.
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.