Data-driven multifidelity topology design using a deep generative model: Application to forced convection heat transfer problems
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Title
Data-driven multifidelity topology design using a deep generative model: Application to forced convection heat transfer problems
Authors
Keywords
Topology optimization, Data-driven approach, Multifidelity design, Variational autoencoder, Turbulent heat transfer, Minimax problem
Journal
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
Volume 388, Issue -, Pages 114284
Publisher
Elsevier BV
Online
2021-11-12
DOI
10.1016/j.cma.2021.114284
References
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