A machine learning-based surrogate model for optimization of truss structures with geometrically nonlinear behavior

Design optimization of geometrically nonlinear structures is well known as a computationally expensive problem by using incremental-iterative solution techniques. To handle the problem effectively the optimization algorithm needs to ensure that the trade-off between the computational time and the qu...

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Vydané v:	Finite elements in analysis and design Ročník 196; s. 103572
Hlavní autori:	Mai, Hau T., Kang, Joowon, Lee, Jaehong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Amsterdam Elsevier B.V 15.11.2021 Elsevier BV
Predmet:	Artificial neural networks Computational efficiency Computing costs Computing time Deep neural network Design optimization Evolutionary algorithms Evolutionary computation Finite element method Geometric nonlinear Iterative methods Iterative solution Machine learning Neural network Neural networks Surrogate model Truss optimization Trusses Deep neural network Neural network Surrogate model Geometric nonlinear Machine learning Truss optimization
ISSN:	0168-874X, 1872-6925
On-line prístup:	Získať plný text
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