Data-driven multifidelity topology design using a deep generative model: application to forced convection heat transfer problems

DOI10.1016/j.cma.2021.114284OpenAlexW3214354305MaRDI QIDQ2060177

Shintaro Yamasaki, Kentaro Yaji, Kikuo Fujita

Publication date: 13 December 2021

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cma.2021.114284

zbMATH Keywords

minimax problemtopology optimizationturbulent heat transferdata-driven approachvariational autoencodermultifidelity design

Mathematics Subject Classification ID

Topological methods for optimization problems in solid mechanics (74P15) Forced convection (76R05)

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