Graph neural networks and implicit neural representation for near-optimal topology prediction over irregular design domains
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Seo, Minsik | - |
dc.contributor.author | Min, Seungjae | - |
dc.date.accessioned | 2023-10-04T07:06:07Z | - |
dc.date.available | 2023-10-04T07:06:07Z | - |
dc.date.created | 2023-05-03 | - |
dc.date.issued | 2023-08 | - |
dc.identifier.issn | 0952-1976 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/191728 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Elsevier Ltd | - |
dc.title | Graph neural networks and implicit neural representation for near-optimal topology prediction over irregular design domains | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Min, Seungjae | - |
dc.identifier.doi | 10.1016/j.engappai.2023.106284 | - |
dc.identifier.scopusid | 2-s2.0-85151749778 | - |
dc.identifier.wosid | 000979710100001 | - |
dc.identifier.bibliographicCitation | Engineering Applications of Artificial Intelligence, v.123, no.PartA, pp.1 - 14 | - |
dc.relation.isPartOf | Engineering Applications of Artificial Intelligence | - |
dc.citation.title | Engineering Applications of Artificial Intelligence | - |
dc.citation.volume | 123 | - |
dc.citation.number | PartA | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 14 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Automation & Control Systems | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Automation & Control Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | Design domains | - |
dc.subject.keywordPlus | Fourier features | - |
dc.subject.keywordPlus | Graph neural networks | - |
dc.subject.keywordPlus | Implicit neural representation | - |
dc.subject.keywordPlus | Near-optimal | - |
dc.subject.keywordPlus | Neural representations | - |
dc.subject.keywordPlus | Optimal topologies | - |
dc.subject.keywordPlus | Optimization problems | - |
dc.subject.keywordPlus | Topology optimisation | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Fourier feature | - |
dc.subject.keywordAuthor | Graph neural networks | - |
dc.subject.keywordAuthor | Implicit neural representations | - |
dc.subject.keywordAuthor | Topology optimization | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S0952197623004682?via%3Dihub | - |
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