Application of domain-adaptive convolutional variational autoencoder for stress-state prediction
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Sang Min | - |
dc.contributor.author | Park, Sang-Youn | - |
dc.contributor.author | Choi, Byoung-Ho | - |
dc.date.accessioned | 2022-06-22T05:41:13Z | - |
dc.date.available | 2022-06-22T05:41:13Z | - |
dc.date.created | 2022-06-22 | - |
dc.date.issued | 2022-07-19 | - |
dc.identifier.issn | 0950-7051 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/142225 | - |
dc.description.abstract | Applying data-driven methods such as deep learning in material mechanics is challenging because producing a sufficiently large, labeled dataset is costly resource-wise. This paper outlines a new approach to overcoming this difficulty by transferring knowledge from a source domain of finite-element-analysis data to a target domain of real-world test-specimen images so that a model capable of accurate and robust predictions in both domains may be constructed. To achieve this transfer of knowledge, discrepancy-based unsupervised domain adaptation is adopted into a convolutional variational autoencoder structure. To evaluate the proposed approach, a four-point bending experiment was conducted on 6061 aluminum alloy and 316 stainless steel to produce 550 unlabeled target-domain data images. The same bending situation was analyzed using the finite-element method implemented in the commercial software package ABAQUS to produce 6000 labeled, source-domain data images. The proposed domain-adaptive convolutional variational autoencoder was trained using the maximum mean discrepancy method on the target-and the source-domain data. The predictions using the domain-adapted convolutional variational autoencoder were relatively more accurate than those using the model trained only on the source domain. It is expected that the proposed approach can address the scarcity of labeled data in various applications of material mechanics and provide a base technology for the development of various data-driven approaches.(C) 2022 Elsevier B.V. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER | - |
dc.subject | NEURAL-NETWORK | - |
dc.subject | KERNEL | - |
dc.subject | SYSTEM | - |
dc.title | Application of domain-adaptive convolutional variational autoencoder for stress-state prediction | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Choi, Byoung-Ho | - |
dc.identifier.doi | 10.1016/j.knosys.2022.108827 | - |
dc.identifier.scopusid | 2-s2.0-85129750653 | - |
dc.identifier.wosid | 000799850400016 | - |
dc.identifier.bibliographicCitation | KNOWLEDGE-BASED SYSTEMS, v.248 | - |
dc.relation.isPartOf | KNOWLEDGE-BASED SYSTEMS | - |
dc.citation.title | KNOWLEDGE-BASED SYSTEMS | - |
dc.citation.volume | 248 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.subject.keywordPlus | NEURAL-NETWORK | - |
dc.subject.keywordPlus | KERNEL | - |
dc.subject.keywordPlus | SYSTEM | - |
dc.subject.keywordAuthor | Unsupervised domain adaptation | - |
dc.subject.keywordAuthor | Stress analysis | - |
dc.subject.keywordAuthor | Four-point bending | - |
dc.subject.keywordAuthor | Variational autoencoder | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Convolutional neural network | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.