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
Hauptverfasser: Ramu, Palaniappan, Thananjayan, Pugazhenthi, Acar, Erdem, Bayrak, Gamze, Park, Jeong Woo, Lee, Ikjin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2022
Springer Nature B.V
Schlagworte:
Algorithms
Computational Mathematics and Numerical Analysis
Design optimization
Engineering
Engineering Design
Fluid mechanics
Keywords
Literature reviews
Machine learning
Mechanical engineering
Neural networks
Review Paper
Software utilities
Structural design
Theoretical and Applied Mechanics
Uncertainty
Reinforcement learning
Regression
Variational autoencoder
Neural network
Clustering
Supervised/unsupervised learning
Optimization
Deep learning
Design diversity
Dimension reduction
Classification
Machine learning
Generative design
ISSN:1615-147X, 1615-1488
Online-Zugang:Volltext
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Zusammenfassung: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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ISSN:1615-147X
1615-1488
DOI:10.1007/s00158-022-03369-9