Graph neural networks and implicit neural representation for near-optimal topology prediction over irregular design domains
- Authors
- Seo, Minsik; Min, Seungjae
- Issue Date
- Aug-2023
- Publisher
- Elsevier Ltd
- Keywords
- Deep learning; Fourier feature; Graph neural networks; Implicit neural representations; Topology optimization
- Citation
- Engineering Applications of Artificial Intelligence, v.123, no.PartA, pp.1 - 14
- Indexed
- SCIE
SCOPUS
- Journal Title
- Engineering Applications of Artificial Intelligence
- Volume
- 123
- Number
- PartA
- Start Page
- 1
- End Page
- 14
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/191728
- DOI
- 10.1016/j.engappai.2023.106284
- ISSN
- 0952-1976
- Abstract
- This paper proposes a deep neural network-based topology optimization acceleration method for irregular design domains that predicts (near-)optimal topologies. A topology optimization problem is a complex non-Euclidean data, which can be embedded in a graph form, and a graph neural network encodes it to Euclidean data such as vectors and matrices. The encoded information is applied to a multi-layer perceptron-based implicit neural representation model, and the multi-layer perceptron approximator predicts the compliance optimal material distribution. The prediction performance of the proposed encoder-approximator architecture is evaluated for several topology optimization problems. The trained network provides 96.6% compliance accuracy, except for 8.0% of the outliers. The two criteria have been investigated to estimate potential outliers, and post-optimization can resolve the outlier within fewer iterations than the original optimization.
- Files in This Item
-
Go to Link
- Appears in
Collections - 서울 공과대학 > 서울 미래자동차공학과 > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.