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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