[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122647-en":3,"doc-seo-122647-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122647,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Topology Optimization via Machine Learning and Deep Learning - A Review","Topology optimization (TO) derives an optimal material layout that meets specified loads and boundary conditions within a design domain. While TO can enable effective design without an initial design, its adoption is constrained by high computational cost, which is largely driven by repeated finite element analyses for sensitivity evaluation. Advances in machine learning, especially deep learning, have motivated research on ML-based TO (MLTO) to achieve faster, more efficient optimization. This review analyzes prior MLTO work from both TO and ML viewpoints, and discusses limitations and future directions.","Topology Optimization via Machine Learning and Deep Learning:  \nA Review  \nSeungyeon Shin1,a, Dongju Shin1,2,a, and Namwoo Kang1,2, *  \n1 Cho Chun Shik Graduate School of Mobility, Korea Advanced Institute of Science and Technology  \n2 Narnia Labs  \n*[Corresponding author: ](Corresponding author: nwkang@kaist.ac.kr)[nwkang@kaist.ac.kr](Corresponding author: nwkang@kaist.ac.kr)a Contributed equally to this work.  \nA previous version of this manuscript was presented at the 2021 World Congress on Advances in Structural Engineering and Mechanics (ASEM21) (Seoul, Korea, August 23-26, 2021) (Shin et al., 2021)  \nAbstract  \nTopology 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:  \n(1) TO and (2) ML perspectives. The TO perspective addresses “why” touse 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.  \n1. Introduction  \nTopology optimization (TO) is a field of design optimization that determines the optimal material layout under certain constraints on loads and boundaries within a given design space. This method allows the optimal distribution of materials with desired performance to be determined while meeting the design constraints of the structure (Bendsøe, 1989) . TO is meaningful in that, compared with conventional optimization approaches, designing is possible without meaningful initial design. Due to these advantages, various TO methodologies have been studied to date (Bendsøe, 1989; Rozvany et al., 1992; Mlejnek, 1992; Allaire et al., 2002; Wang et al., 2003; Xie & Steven, 1993) . The following four TO methodologies are described in detail in Appendix A: density-based method (i.e., the solid isotropic material with penalization (SIMP) method), evolutionary structural optimization (ESO) method, level-set method (LSM), and moving morphable component (MMC) method.  \nRecent TO methods aim to solve various industrial applications. Examples include TO for patient-specific osteosynthesis plates (Park et al., 2021), microscale lattice parameter (i.e., the strut diameter) optimization for TO (Cheng et al., 2019), homogenization of 3D TO with microscale lattices (Zhang et al., 2021b), and multiscale TO for additive manufacturing (AM) (Kim et al., 2022) . Other notable works for more complex TO problems include a multilevel approach to large-scale TO accounting for linearized buckling criteria (Ferrari & Sigmund, 2020), the localized parametric level-set method applying a B-spline interpolation method (Wu et al., 2020), the systematic TO approach for simultaneously designing morphing functionality and actuation in three-dimensional wing structures (Jensen et al., 2021), the parametrized level-set method combined with the MMA algorithm to solve nonlinear heat conduction problems with regional temperature constraints (Zhuang et al., 2021b), and the parametric level-set method for non-uniform mesh of fluid TO problems (Li et al., 2022) .  \nHowever, although the aforementioned TO methodologies can produce good conceptual designs , one of the main challenges in performing TO is its high computational cost. The overall cost of the computational scheme is dominated by finite element analysis (FEA), which computes the sensitivity for each iteration of the optimization process. The required FEA time increases as the mesh size increases (e.g., ","cbCaib02A7MpgPf8","https://ap.wps.com/l/cbCaib02A7MpgPf8","pdf",2362512,1,50,"English","en",105,"# Introduction\n## Topology optimization fundamentals\n## Computational cost and acceleration approaches\n## Motivation for ML-based topology optimization","[{\"question\":\"What problem does topology optimization aim to solve?\",\"answer\":\"Topology optimization determines an optimal material layout that satisfies given load and boundary conditions within a design domain.\"},{\"question\":\"Why is topology optimization computationally challenging?\",\"answer\":\"Most of the cost comes from repeated finite element analysis to compute sensitivities at each optimization iteration, and the runtime grows quickly with mesh size.\"},{\"question\":\"How does machine learning relate to speeding up topology optimization?\",\"answer\":\"Machine learning methods are explored to accelerate TO by enabling rapid optimization without relying solely on expensive repeated analysis, addressing the high computational burden.\"}]","Topology Optimization via Machine Learning and Deep Learning - 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