Development of advanced structures using modern manufacturing methods has become attractive since they allow to improve system efficiency and performance, fuel consumption reduction, lightweighting to decrease weight and durability of structures, and many more. Designing tools such as topology optimization (TO) has contributed to such developments and facilitated in adapting new manufacturing methods such as 3D printing and computer numerical control machining in many areas of engineering and industry.

TO requires computational resources, which can be significantly complex and time consuming when complicated designs and multiphysics problems are considered. To overcome these difficulties, computational acceleration techniques have been applied together with high performance computing. In the current work, various up-to-date research studies in computational acceleration of TO methods are analysed, classified and research trends are evaluated. Thus, the results of the work clearly shows that earlier works relied on central processing unit (CPU)-based computational acceleration techniques, while latest research studies mostly consider graphics processing unit (GPU) and machine learning (ML)-based approaches. The latter got significant attention within last few years and becoming one of the research areas in computational TO. From the reviewed works, it can be concluded that in all of the acceleration techniques, solid mechanics problems were mostly studied, while a few number of research studies are dedicated to heat transfer, fluid flow and electro thermomechanical applications.

使用现代制造方法开发先进结构已变得很有吸引力，因为它们可以提高系统效率和性能、降低燃料消耗、轻量化以减轻结构的重量和耐用性等等。拓扑优化 (TO) 等设计工具有助于此类发展，并有助于在工程和工业的许多领域采用新的制造方法，例如 3D 打印和计算机数控加工。

TO 需要计算资源，当考虑复杂的设计和多物理场问题时，这可能非常复杂且耗时。为了克服这些困难，计算加速技术已与高性能计算一起应用。在目前的工作中，对TO方法计算加速的各种最新研究进行了分析、分类和评估研究趋势。因此，这项工作的结果清楚地表明，早期的工作依赖于基于中央处理器 (CPU) 的计算加速技术，而最新的研究主要考虑基于图形处理器 (GPU) 和机器学习 (ML) 的方法。后者在过去几年中得到了极大的关注，并成为计算 TO 的研究领域之一。