论文标题
部分可观测时空混沌系统的无模型预测
Topology Optimization via Machine Learning and Deep Learning: A Review
论文作者
论文摘要
拓扑优化（TO）是一种得出满足设计域内给定负载和边界条件的最佳设计的方法。此方法可实现无初始设计的有效设计，但由于高计算成本而受到限制。同时，包括深度学习在内的机器学习（ML）方法在21世纪取得了长足的进步，因此，已经进行了许多研究，以通过将ML应用于ML来实现有效和快速优化。因此，本研究回顾并分析了对基于ML的（MLTO）的先前研究。 MLTO的两种不同观点用于审查研究：（1）至（2）ML观点。对观点的地址是“为什么”使用ML进行的“为什么”，而ML透视图则“如何将ML应用于”。此外，研究了当前的MLTO研究和未来研究方向的局限性。
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.
