A survey of machine learning techniques in structural and multidisciplinary optimization
Machine Learning (ML) techniques have been used in an extensive range of applications in the field of structural and multidisciplinary optimization over the last few years. This paper presents a survey of this wide but disjointed literature on ML techniques in the structural and multidisciplinary op...
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| Veröffentlicht in: | Structural and multidisciplinary optimization Jg. 65; H. 9 |
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| Hauptverfasser: | , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.09.2022
Springer Nature B.V |
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| ISSN: | 1615-147X, 1615-1488 |
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| Abstract | Machine Learning (ML) techniques have been used in an extensive range of applications in the field of structural and multidisciplinary optimization over the last few years. This paper presents a survey of this wide but disjointed literature on ML techniques in the structural and multidisciplinary optimization field. First, we discuss the challenges associated with conventional optimization and how Machine learning can address them. Then, we review the literature in the context of how ML can accelerate design synthesis and optimization. Some real-life engineering applications in structural design, material design, fluid mechanics, aerodynamics, heat transfer, and multidisciplinary design are summarized, and a brief list of widely used open-source codes as well as commercial packages are provided. Finally, the survey culminates with some concluding remarks and future research suggestions. For the sake of completeness, categories of ML problems, algorithms, and paradigms are presented in the Appendix. |
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| AbstractList | Machine Learning (ML) techniques have been used in an extensive range of applications in the field of structural and multidisciplinary optimization over the last few years. This paper presents a survey of this wide but disjointed literature on ML techniques in the structural and multidisciplinary optimization field. First, we discuss the challenges associated with conventional optimization and how Machine learning can address them. Then, we review the literature in the context of how ML can accelerate design synthesis and optimization. Some real-life engineering applications in structural design, material design, fluid mechanics, aerodynamics, heat transfer, and multidisciplinary design are summarized, and a brief list of widely used open-source codes as well as commercial packages are provided. Finally, the survey culminates with some concluding remarks and future research suggestions. For the sake of completeness, categories of ML problems, algorithms, and paradigms are presented in the Appendix. |
| ArticleNumber | 266 |
| Author | Ramu, Palaniappan Acar, Erdem Bayrak, Gamze Park, Jeong Woo Lee, Ikjin Thananjayan, Pugazhenthi |
| Author_xml | – sequence: 1 givenname: Palaniappan surname: Ramu fullname: Ramu, Palaniappan organization: Advanced Design, Optimization and Probabilistic Techniques Laboratory, Department of Engineering Design, Indian Institute of Technology Madras – sequence: 2 givenname: Pugazhenthi surname: Thananjayan fullname: Thananjayan, Pugazhenthi organization: Advanced Design, Optimization and Probabilistic Techniques Laboratory, Department of Engineering Design, Indian Institute of Technology Madras – sequence: 3 givenname: Erdem surname: Acar fullname: Acar, Erdem organization: Department of Mechanical Engineering, TOBB University of Economics and Technology – sequence: 4 givenname: Gamze surname: Bayrak fullname: Bayrak, Gamze organization: Department of Mechanical Engineering, TOBB University of Economics and Technology – sequence: 5 givenname: Jeong Woo surname: Park fullname: Park, Jeong Woo organization: Mechanical Engineering Department, Korea Advanced Institute of Science and Technology – sequence: 6 givenname: Ikjin orcidid: 0000-0002-3470-7341 surname: Lee fullname: Lee, Ikjin email: ikjin.lee@kaist.ac.kr organization: Mechanical Engineering Department, Korea Advanced Institute of Science and Technology |
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| Keywords | Uncertainty Reinforcement learning Regression Variational autoencoder Neural network Clustering Supervised/unsupervised learning Optimization Deep learning Design diversity Dimension reduction Classification Machine learning Generative design |
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