Artificial neural network to predict the structural compliance of irregular geometries considering volume constraints-Reference-Cited by-同舟云学术

Artificial neural network to predict the structural compliance of irregular geometries considering volume constraints

Published:2024 Issue:4 Volume:11 Page:24-00002-24-00002
ISSN:2187-9745
Container-title:Mechanical Engineering Journal
language:en
Short-container-title:

Author:

CUI Yi¹,TAKEUCHI Ichiro¹,YANG Wenzhi²,GU Shaojie¹,YOON Sungmin¹,MATSUMOTO Toshiro¹

Affiliation:

1. Department of Mechanical Science and Engineering, Nagoya University

2. Key Laboratory of Mechanics on Disaster and Environment in Western China, College of Civil Engineering and Mechanics, Lanzhou University

Publisher

Japan Society of Mechanical Engineers

Link

https://www.jstage.jst.go.jp/article/mej/11/4/11_24-00002/_pdf

Reference20 articles.

1. Anitescu, C., Atroshchenko, E. and Rabczuk, T., Artificial neural network methods for the solution of second order boundary value problems. Computers, Materials & Continua, Vol. 59, No. 1 (2019), pp. 345-359.

2. Cui, Y., Takahashi, T. and Matsumoto, T., A time-saving FEM-based approach for structural topology optimization with exact boundary representation. Mechanical Engineering Journal, Vol. 9 No.6 (2022), DOI:10.1299/mej.22-00281

3. Cui, Y., Takahashi, T. and Matsumoto, T., An exact volume constraint method for topology optimization via reaction–diffusion equation. Computers & Structures Vol. 280 (2023), p. 1006986.

4. Cui, Y., Yang, W., Takahashi, T. and Matsumoto, T., Topology optimization of anisotropic structure for arbitrary objective functionals with exact free boundary representation. Computers & Structures Vol. 300 (2024), p. 107405.

5. Deng, Y., Zhang, W. and Wang, X., A review on applications of deep learning in structural topology optimization. Computer-Aided Design, Vol. 117, (2020), p. 102961.

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