Application of domain-adaptive convolutional variational autoencoder for stress-state prediction
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-a...
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| Veröffentlicht in: | Knowledge-based systems Jg. 248; S. 108827 |
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| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Amsterdam
Elsevier B.V
19.07.2022
Elsevier Science Ltd |
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| ISSN: | 0950-7051, 1872-7409 |
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| 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. |
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| AbstractList | 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. |
| ArticleNumber | 108827 |
| Author | Park, Sang-Youn Choi, Byoung-Ho Lee, Sang Min |
| Author_xml | – sequence: 1 givenname: Sang Min surname: Lee fullname: Lee, Sang Min – sequence: 2 givenname: Sang-Youn orcidid: 0000-0002-2948-6636 surname: Park fullname: Park, Sang-Youn – sequence: 3 givenname: Byoung-Ho orcidid: 0000-0001-8299-0957 surname: Choi fullname: Choi, Byoung-Ho email: bhchoi@korea.ac.kr |
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| Cites_doi | 10.1016/j.asoc.2020.106959 10.1214/aoms/1177729694 10.1109/TPAMI.2018.2889774 10.1016/j.matcom.2020.04.031 10.1016/j.eswa.2021.116269 10.1038/nature14539 10.1016/j.ultras.2021.106610 10.1016/j.actbio.2017.09.025 10.1007/978-3-319-58347-1_10 10.1145/3336191.3371831 10.1109/ICCV.2011.6126474 10.1098/rsta.2015.0202 10.1145/3422622 10.1109/ACCESS.2020.2987324 10.1007/s10994-009-5152-4 10.1145/2835776.2835837 10.1016/j.knosys.2018.12.019 10.1109/LSP.2020.2965328 |
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| Keywords | Deep learning Unsupervised domain adaptation Variational autoencoder Stress analysis Convolutional neural network Four-point bending |
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| References | Chollet (b45) 2017 Rai, Sahu (b4) 2020; 8 Goodfellow, Pouget-Abadie, Mirza, Xu, Warde-Farley, Ozair, Courville, Bengio (b16) 2020; 63 M. Long, Y. Cao, J. Wang, M.I. Jordan, Learning transferable features with deep adaptation networks, in: Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, vol. 1, 2015, pp. 97–105. Steinwart (b42) 2002; 2 Panigrahi, Nanda, Swarnkar (b33) 2021; 194 M.D. Zeiler, G.W. Taylor, R. Fergus, R, Adaptive deconvolutional networks for mid and high level feature learning, in: Proceedings of 2011 International Conference on Computer Vision, 2011, pp. 2018–2025. Shui, Chen, Wen, Zhou, Gagné, Wang (b37) 2020 Kingma, Welling (b38) 2013 Zhang, Butepage, Kjellstrom, Mandt (b39) 2019; 41 Lim, Jiang, Yi (b25) 2020; 27 Noh, Hong, Han (b32) 2015 Gong, Shi, Sha, Grauman (b15) 2012 Lecun, Bengio, Hinton (b28) 2015; 521 Sun, Saenko (b6) 2016; 9915 An, Cho (b24) 2015 Pan, Chen, Zhang, Liu, He, Lv (b19) 2021 . Long, Zhu, Wang, Jordan (b3) 2017 Ben-David, Blitzer, Crammer, Kulesza, Pereira, Vaughan (b5) 2010; 79 Dai, Fidler, Urtasun, Lin (b23) 2017 Gopalan, Li, Chellappa (b14) 2011 Ganin, Ustinova, Ajakan, Germain, Larochelle, Laviolette, Marchand, Lempitsky (b10) 2016; 17 A. Radford, L. Metz, S. Chintala, Unsupervised representation learning with deep convolutional generative adversarial networks, in: Proceedings of the 4th International Conference on Learning Representations, 2016, pp. 1–16. Valueva, Nagornov, Lyakhov, Valuev, Chervyakov (b27) 2020; 177 Han, Liu, Yang, Jiang (b11) 2019; 165 Y. Wu, C. DuBois, A.X. Zheng, M. Ester, Collaborative denoising auto-encoders for top-N recommender systems, in: WSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining, Feb., 2016, pp. 153–162 Ganin (b35) 2017; 17 Odena (b21) 2016 Zhao, Adeli, Honnorat, Leng, Pohl (b26) 2019; 11765 Long, Zhu, Wang, Jordan (b34) 2016 Mai Ngoc, Hwang (b48) 2020 A. Odena, C. Olah, J. Shlens, Conditional image synthesis with auxiliary classifier gans, in: Proceedings of the 34th International Conference on Machine Learning, vol. 6, 2017, pp. 4043–4055. Venkatesan, Li (b29) 2005 Zeiler, Fergus (b30) 2014; 8689 Gretton, Borgwardt, Rasch, Schölkopf, Smola (b41) 2012; 13 Luo, Pi, Pan, Xie, Yu, Liu (b17) 2022; 191 Kullback, Leibler (b40) 1951; 22 Jolliffe, Cadima (b49) 2016; 374 Posilović, Medak, Subašić, Budimir, Lončarić (b18) 2022; 119 Huang, Smola, Gretton, Borgwardt, Schölkopf (b13) 2007 Motiian, Piccirilli, Adjeroh, Doretto (b36) 2017 Liang, Liu, Sun (b1) 2017; 63 Jiang, Kim, Asnani, Kannan, Oh, Viswanath (b46) 2019 J. Jiang, C.X. Zhai, Instance weighting for domain adaptation in NLP, in: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, June, 2007, pp. 264–271. I. Shenbin, A. Alekseev, E. Tutubalina, V. Malykh, S.I. Nikolenko, RecVAE: A new variational autoencoder for top-n recommendations with implicit feedback, in: WSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining, Jan., 2020, pp. 528–536 Zhang, Shen, Zhou, Jin (b2) 2021; 100 Tzeng, Hoffman, Zhang, Saenko, Darrell (b7) 2014 Zhu, Thung, Adeli, Zhang, Shen (b47) 2017; 10435 Y. Ganin, V. Lempitsky, Unsupervised domain adaptation by backpropagation, in: Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, vol. 2, 2015, pp. 1180–1189. Long (10.1016/j.knosys.2022.108827_b3) 2017 Liang (10.1016/j.knosys.2022.108827_b1) 2017; 63 Gretton (10.1016/j.knosys.2022.108827_b41) 2012; 13 Luo (10.1016/j.knosys.2022.108827_b17) 2022; 191 Chollet (10.1016/j.knosys.2022.108827_b45) 2017 Zeiler (10.1016/j.knosys.2022.108827_b30) 2014; 8689 Motiian (10.1016/j.knosys.2022.108827_b36) 2017 Jolliffe (10.1016/j.knosys.2022.108827_b49) 2016; 374 Kullback (10.1016/j.knosys.2022.108827_b40) 1951; 22 Ben-David (10.1016/j.knosys.2022.108827_b5) 2010; 79 Noh (10.1016/j.knosys.2022.108827_b32) 2015 Rai (10.1016/j.knosys.2022.108827_b4) 2020; 8 Posilović (10.1016/j.knosys.2022.108827_b18) 2022; 119 10.1016/j.knosys.2022.108827_b20 10.1016/j.knosys.2022.108827_b22 Huang (10.1016/j.knosys.2022.108827_b13) 2007 Zhang (10.1016/j.knosys.2022.108827_b39) 2019; 41 10.1016/j.knosys.2022.108827_b9 Ganin (10.1016/j.knosys.2022.108827_b10) 2016; 17 10.1016/j.knosys.2022.108827_b8 Jiang (10.1016/j.knosys.2022.108827_b46) 2019 Dai (10.1016/j.knosys.2022.108827_b23) 2017 Steinwart (10.1016/j.knosys.2022.108827_b42) 2002; 2 Mai Ngoc (10.1016/j.knosys.2022.108827_b48) 2020 Han (10.1016/j.knosys.2022.108827_b11) 2019; 165 Zhao (10.1016/j.knosys.2022.108827_b26) 2019; 11765 Gopalan (10.1016/j.knosys.2022.108827_b14) 2011 10.1016/j.knosys.2022.108827_b31 Odena (10.1016/j.knosys.2022.108827_b21) 2016 Zhu (10.1016/j.knosys.2022.108827_b47) 2017; 10435 Pan (10.1016/j.knosys.2022.108827_b19) 2021 Shui (10.1016/j.knosys.2022.108827_b37) 2020 Ganin (10.1016/j.knosys.2022.108827_b35) 2017; 17 An (10.1016/j.knosys.2022.108827_b24) 2015 Kingma (10.1016/j.knosys.2022.108827_b38) 2013 Lecun (10.1016/j.knosys.2022.108827_b28) 2015; 521 Tzeng (10.1016/j.knosys.2022.108827_b7) 2014 Valueva (10.1016/j.knosys.2022.108827_b27) 2020; 177 Gong (10.1016/j.knosys.2022.108827_b15) 2012 Goodfellow (10.1016/j.knosys.2022.108827_b16) 2020; 63 10.1016/j.knosys.2022.108827_b43 Zhang (10.1016/j.knosys.2022.108827_b2) 2021; 100 10.1016/j.knosys.2022.108827_b44 Venkatesan (10.1016/j.knosys.2022.108827_b29) 2005 Long (10.1016/j.knosys.2022.108827_b34) 2016 Lim (10.1016/j.knosys.2022.108827_b25) 2020; 27 Panigrahi (10.1016/j.knosys.2022.108827_b33) 2021; 194 Sun (10.1016/j.knosys.2022.108827_b6) 2016; 9915 10.1016/j.knosys.2022.108827_b12 |
| References_xml | – volume: 10435 start-page: 72 year: 2017 end-page: 80 ident: b47 article-title: Maximum mean discrepancy based multiple kernel learning for incomplete multimodality neuroimaging data publication-title: LNCS – reference: M. Long, Y. Cao, J. Wang, M.I. Jordan, Learning transferable features with deep adaptation networks, in: Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, vol. 1, 2015, pp. 97–105. – start-page: 999 year: 2011 end-page: 1006 ident: b14 article-title: Domain adaptation for object recognition: An unsupervised approach publication-title: Proceedings of the IEEE International Conference on Computer Vision – start-page: 5716 year: 2017 end-page: 5726 ident: b36 article-title: Unified deep supervised domain adaptation and generalization publication-title: Proceedings of the IEEE International Conference on Computer Vision, vol. 2017 – volume: 27 start-page: 231 year: 2020 end-page: 235 ident: b25 article-title: Deep clustering with variational autoencoder publication-title: IEEE Signal Process. Lett. – start-page: 1520 year: 2015 end-page: 1528 ident: b32 article-title: Learning deconvolution network for semantic segmentation publication-title: Proceedings of the IEEE International Conference on Computer Vision, vol. 2015 – year: 2017 ident: b3 article-title: Unsupervised domain adaptation with residual transfer networks – volume: 79 start-page: 151 year: 2010 end-page: 175 ident: b5 article-title: A theory of learning from different domains publication-title: Mach. Learn. – volume: 2 start-page: 67 year: 2002 end-page: 93 ident: b42 article-title: On the influence of the kernel on the consistency of support vector machines publication-title: J. Mach. Learn. Res. – volume: 41 start-page: 2008 year: 2019 end-page: 2026 ident: b39 article-title: Advances in variational inference publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 11765 start-page: 823 year: 2019 end-page: 831 ident: b26 article-title: Variational AutoEncoder for regression: Application to brain aging analysis publication-title: LNCS – reference: J. Jiang, C.X. Zhai, Instance weighting for domain adaptation in NLP, in: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, June, 2007, pp. 264–271. – year: 2021 ident: b19 article-title: Generative adversarial network in mechanical fault diagnosis under small sample: A systematic review on applications and future perspectives publication-title: ISA Trans. – volume: 374 year: 2016 ident: b49 article-title: Principal component analysis: A review and recent developments publication-title: Philos. Trans. R. Soc. A – volume: 9915 start-page: 443 year: 2016 end-page: 450 ident: b6 article-title: Deep CORAL: Correlation alignment for deep domain adaptation publication-title: LNCS – year: 2020 ident: b37 article-title: Beyond H-divergence: Domain adaptation theory with Jensen-Shannon divergence – volume: 119 year: 2022 ident: b18 article-title: Generating ultrasonic images indistinguishable from real images using generative adversarial networks publication-title: Ultrasonics – reference: A. Odena, C. Olah, J. Shlens, Conditional image synthesis with auxiliary classifier gans, in: Proceedings of the 34th International Conference on Machine Learning, vol. 6, 2017, pp. 4043–4055. – year: 2016 ident: b21 article-title: Semi-supervised learning with generative adversarial networks – year: 2019 ident: b46 article-title: Turbo autoencoder: Deep learning based channel codes for point-to-point communication channels – reference: I. Shenbin, A. Alekseev, E. Tutubalina, V. Malykh, S.I. Nikolenko, RecVAE: A new variational autoencoder for top-n recommendations with implicit feedback, in: WSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining, Jan., 2020, pp. 528–536, – volume: 63 start-page: 139 year: 2020 end-page: 144 ident: b16 article-title: Generative adversarial networks publication-title: Commun. ACM – year: 2015 ident: b24 article-title: Variational autoencoder based anomaly detection using reconstruction probability publication-title: 2015-2 Special Lecture on IE – volume: 8 start-page: 71050 year: 2020 end-page: 71073 ident: b4 article-title: Driven by data or derived through Physics? A review of hybrid physics guided machine learning techniques with cyber-physical system (CPS) focus publication-title: IEEE Access – start-page: 2066 year: 2012 end-page: 2073 ident: b15 article-title: Geodesic flow kernel for unsupervised domain adaptation publication-title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition – reference: Y. Wu, C. DuBois, A.X. Zheng, M. Ester, Collaborative denoising auto-encoders for top-N recommender systems, in: WSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining, Feb., 2016, pp. 153–162, – year: 2017 ident: b23 article-title: Towards diverse and natural image descriptions via a conditional GaN – year: 2016 ident: b34 article-title: Unsupervised domain adaptation with residual transfer networks – volume: 8689 start-page: 818 year: 2014 end-page: 833 ident: b30 article-title: Visualizing and understanding convolutional networks publication-title: LNCS – volume: 194 start-page: 781 year: 2021 end-page: 789 ident: b33 article-title: A survey on transfer learning publication-title: SIST – volume: 22 start-page: 79 year: 1951 end-page: 86 ident: b40 article-title: On information and sufficiency publication-title: Ann. Math. Stat. – volume: 100 year: 2021 ident: b2 article-title: Application of LSTM approach for modelling stress-strain behaviour of soil publication-title: Appl. Soft Comput. – volume: 17 start-page: 189 year: 2017 end-page: 209 ident: b35 article-title: Domain-adversarial training of neural networks publication-title: Adv. Comput. Vis. Pattern Recognit. – reference: M.D. Zeiler, G.W. Taylor, R. Fergus, R, Adaptive deconvolutional networks for mid and high level feature learning, in: Proceedings of 2011 International Conference on Computer Vision, 2011, pp. 2018–2025. – reference: A. Radford, L. Metz, S. Chintala, Unsupervised representation learning with deep convolutional generative adversarial networks, in: Proceedings of the 4th International Conference on Learning Representations, 2016, pp. 1–16. – year: 2005 ident: b29 article-title: Convolutional Neural Networks in Visual Computing – volume: 17 start-page: 1 year: 2016 end-page: 35 ident: b10 article-title: Domain-adversarial training of neural networks publication-title: J. Mach. Learn. Res. – volume: 165 start-page: 474 year: 2019 end-page: 487 ident: b11 article-title: A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults publication-title: Knowl. Based. Syst. – reference: Y. Ganin, V. Lempitsky, Unsupervised domain adaptation by backpropagation, in: Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, vol. 2, 2015, pp. 1180–1189. – volume: 63 start-page: 227 year: 2017 end-page: 235 ident: b1 article-title: A deep learning approach to estimate chemically treated collagenous tissue nonlinear anisotropic stress-strain responses from microscopy images publication-title: Acta Biomater. – start-page: 453 year: 2020 end-page: 464 ident: b48 article-title: Finding the best k for the dimension of the latent space in autoencoders publication-title: Computational Collective Intelligence – reference: . – year: 2013 ident: b38 article-title: Auto-encoding variational bayes – volume: 13 start-page: 723 year: 2012 end-page: 773 ident: b41 article-title: A kernel two-sample test publication-title: J. Mach. Learn. Res. – volume: 191 year: 2022 ident: b17 article-title: ClawGAN: Claw connection-based generative adversarial networks for facial image translation in thermal to RGB visible light publication-title: Expert Syst. Appl. – year: 2017 ident: b45 article-title: A ten-minute introduction to sequence-to-sequence learning in Keras publication-title: The Keras Blog – start-page: 601 year: 2007 end-page: 608 ident: b13 article-title: Correcting sample selection bias by unlabeled data publication-title: Proceedings of Advances in Neural Information Processing Systems, vol. 19 – year: 2014 ident: b7 article-title: Deep domain confusion: Maximizing for domain invariance – volume: 177 start-page: 232 year: 2020 end-page: 243 ident: b27 article-title: Application of the residue number system to reduce hardware costs of the convolutional neural network implementation publication-title: Math. Comput. Simulation – volume: 521 start-page: 436 year: 2015 end-page: 444 ident: b28 article-title: Deep learning publication-title: Nature – volume: 100 year: 2021 ident: 10.1016/j.knosys.2022.108827_b2 article-title: Application of LSTM approach for modelling stress-strain behaviour of soil publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2020.106959 – year: 2013 ident: 10.1016/j.knosys.2022.108827_b38 – ident: 10.1016/j.knosys.2022.108827_b8 – year: 2017 ident: 10.1016/j.knosys.2022.108827_b3 – start-page: 999 year: 2011 ident: 10.1016/j.knosys.2022.108827_b14 article-title: Domain adaptation for object recognition: An unsupervised approach – volume: 22 start-page: 79 year: 1951 ident: 10.1016/j.knosys.2022.108827_b40 article-title: On information and sufficiency publication-title: Ann. Math. Stat. doi: 10.1214/aoms/1177729694 – volume: 9915 start-page: 443 year: 2016 ident: 10.1016/j.knosys.2022.108827_b6 article-title: Deep CORAL: Correlation alignment for deep domain adaptation publication-title: LNCS – volume: 41 start-page: 2008 year: 2019 ident: 10.1016/j.knosys.2022.108827_b39 article-title: Advances in variational inference publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2018.2889774 – volume: 177 start-page: 232 year: 2020 ident: 10.1016/j.knosys.2022.108827_b27 article-title: Application of the residue number system to reduce hardware costs of the convolutional neural network implementation publication-title: Math. Comput. Simulation doi: 10.1016/j.matcom.2020.04.031 – volume: 191 year: 2022 ident: 10.1016/j.knosys.2022.108827_b17 article-title: ClawGAN: Claw connection-based generative adversarial networks for facial image translation in thermal to RGB visible light publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2021.116269 – year: 2005 ident: 10.1016/j.knosys.2022.108827_b29 – year: 2020 ident: 10.1016/j.knosys.2022.108827_b37 – volume: 17 start-page: 1 year: 2016 ident: 10.1016/j.knosys.2022.108827_b10 article-title: Domain-adversarial training of neural networks publication-title: J. Mach. Learn. Res. – volume: 11765 start-page: 823 year: 2019 ident: 10.1016/j.knosys.2022.108827_b26 article-title: Variational AutoEncoder for regression: Application to brain aging analysis publication-title: LNCS – volume: 521 start-page: 436 year: 2015 ident: 10.1016/j.knosys.2022.108827_b28 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – year: 2021 ident: 10.1016/j.knosys.2022.108827_b19 article-title: Generative adversarial network in mechanical fault diagnosis under small sample: A systematic review on applications and future perspectives publication-title: ISA Trans. – volume: 194 start-page: 781 year: 2021 ident: 10.1016/j.knosys.2022.108827_b33 article-title: A survey on transfer learning publication-title: SIST – volume: 119 year: 2022 ident: 10.1016/j.knosys.2022.108827_b18 article-title: Generating ultrasonic images indistinguishable from real images using generative adversarial networks publication-title: Ultrasonics doi: 10.1016/j.ultras.2021.106610 – start-page: 5716 year: 2017 ident: 10.1016/j.knosys.2022.108827_b36 article-title: Unified deep supervised domain adaptation and generalization – volume: 63 start-page: 227 year: 2017 ident: 10.1016/j.knosys.2022.108827_b1 article-title: A deep learning approach to estimate chemically treated collagenous tissue nonlinear anisotropic stress-strain responses from microscopy images publication-title: Acta Biomater. doi: 10.1016/j.actbio.2017.09.025 – ident: 10.1016/j.knosys.2022.108827_b20 – volume: 17 start-page: 189 issue: 9783319583464 year: 2017 ident: 10.1016/j.knosys.2022.108827_b35 article-title: Domain-adversarial training of neural networks publication-title: Adv. Comput. Vis. Pattern Recognit. doi: 10.1007/978-3-319-58347-1_10 – year: 2016 ident: 10.1016/j.knosys.2022.108827_b34 – ident: 10.1016/j.knosys.2022.108827_b43 doi: 10.1145/3336191.3371831 – start-page: 601 year: 2007 ident: 10.1016/j.knosys.2022.108827_b13 article-title: Correcting sample selection bias by unlabeled data – year: 2014 ident: 10.1016/j.knosys.2022.108827_b7 – year: 2015 ident: 10.1016/j.knosys.2022.108827_b24 article-title: Variational autoencoder based anomaly detection using reconstruction probability – volume: 2 start-page: 67 year: 2002 ident: 10.1016/j.knosys.2022.108827_b42 article-title: On the influence of the kernel on the consistency of support vector machines publication-title: J. Mach. Learn. Res. – volume: 10435 start-page: 72 issue: 2017 year: 2017 ident: 10.1016/j.knosys.2022.108827_b47 article-title: Maximum mean discrepancy based multiple kernel learning for incomplete multimodality neuroimaging data publication-title: LNCS – start-page: 2066 year: 2012 ident: 10.1016/j.knosys.2022.108827_b15 article-title: Geodesic flow kernel for unsupervised domain adaptation – year: 2017 ident: 10.1016/j.knosys.2022.108827_b45 article-title: A ten-minute introduction to sequence-to-sequence learning in Keras – year: 2016 ident: 10.1016/j.knosys.2022.108827_b21 – ident: 10.1016/j.knosys.2022.108827_b31 doi: 10.1109/ICCV.2011.6126474 – volume: 374 year: 2016 ident: 10.1016/j.knosys.2022.108827_b49 article-title: Principal component analysis: A review and recent developments publication-title: Philos. Trans. R. Soc. A doi: 10.1098/rsta.2015.0202 – volume: 63 start-page: 139 year: 2020 ident: 10.1016/j.knosys.2022.108827_b16 article-title: Generative adversarial networks publication-title: Commun. ACM doi: 10.1145/3422622 – volume: 8 start-page: 71050 year: 2020 ident: 10.1016/j.knosys.2022.108827_b4 article-title: Driven by data or derived through Physics? A review of hybrid physics guided machine learning techniques with cyber-physical system (CPS) focus publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2987324 – start-page: 453 year: 2020 ident: 10.1016/j.knosys.2022.108827_b48 article-title: Finding the best k for the dimension of the latent space in autoencoders – volume: 79 start-page: 151 year: 2010 ident: 10.1016/j.knosys.2022.108827_b5 article-title: A theory of learning from different domains publication-title: Mach. Learn. doi: 10.1007/s10994-009-5152-4 – ident: 10.1016/j.knosys.2022.108827_b12 – year: 2017 ident: 10.1016/j.knosys.2022.108827_b23 – year: 2019 ident: 10.1016/j.knosys.2022.108827_b46 – volume: 13 start-page: 723 year: 2012 ident: 10.1016/j.knosys.2022.108827_b41 article-title: A kernel two-sample test publication-title: J. Mach. Learn. Res. – ident: 10.1016/j.knosys.2022.108827_b44 doi: 10.1145/2835776.2835837 – ident: 10.1016/j.knosys.2022.108827_b9 – volume: 165 start-page: 474 year: 2019 ident: 10.1016/j.knosys.2022.108827_b11 article-title: A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults publication-title: Knowl. Based. Syst. doi: 10.1016/j.knosys.2018.12.019 – volume: 27 start-page: 231 year: 2020 ident: 10.1016/j.knosys.2022.108827_b25 article-title: Deep clustering with variational autoencoder publication-title: IEEE Signal Process. Lett. doi: 10.1109/LSP.2020.2965328 – volume: 8689 start-page: 818 year: 2014 ident: 10.1016/j.knosys.2022.108827_b30 article-title: Visualizing and understanding convolutional networks publication-title: LNCS – start-page: 1520 year: 2015 ident: 10.1016/j.knosys.2022.108827_b32 article-title: Learning deconvolution network for semantic segmentation – ident: 10.1016/j.knosys.2022.108827_b22 |
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| SubjectTerms | Aluminum base alloys Convolutional neural network Data Deep learning Domains Finite element method Four-point bending Imagery Knowledge management Labeling Mathematical models Mechanics (physics) Predictions Scarcity Stainless steels Stress analysis Unsupervised domain adaptation Variational autoencoder |
| Title | Application of domain-adaptive convolutional variational autoencoder for stress-state prediction |
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