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Title: A survey of machine learning techniques in structural and multidisciplinary optimization
Authors: Ramu, Palaniappan
Thananjayan, Pugazhenthi
Acar, Erdem
Bayrak, Gamze
Park, Jeong Woo
Lee, Ikjin
Keywords: Classification
Deep learning
Design diversity
Dimension reduction
Generative design
Machine learning
Neural network
Reinforcement learning
unsupervised learning
Variational autoencoder
Uncertainty Quantification
Design Optimization
Surrogate Models
Topology Optimization
Inverse Problems
Issue Date: 2022
Publisher: Springer
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.
ISSN: 1615-147X
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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