A Generative Adversarial Network Approach to EstimateFinite Element Displacement
The finite element analysis(FEA) usually requires complex procedures to set up, and depends on constitutive relation to obtain final simulation results. In order to explore solutions other than the finite element method, the displacement response is considered as a picture generation process with gi...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Control and Information Technology
2019-01-01
|
| Series: | Kongzhi Yu Xinxi Jishu |
| Subjects: | |
| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2019.06.300 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849224887060660224 |
|---|---|
| author | WANG Zhangjun XU Ping WANG Chunpeng ZHAO Ziliang CAI Junkun |
| author_facet | WANG Zhangjun XU Ping WANG Chunpeng ZHAO Ziliang CAI Junkun |
| author_sort | WANG Zhangjun |
| collection | DOAJ |
| description | The finite element analysis(FEA) usually requires complex procedures to set up, and depends on constitutive relation to obtain final simulation results. In order to explore solutions other than the finite element method, the displacement response is considered as a picture generation process with given conditions, bypassing the physical method. Based on generative adversarial network(GAN), and combined with DCGAN and CGAN, a deep learning model was proposed to directly obtain the displacement solution of two-dimensional plane instead of the finite element method. The proposed model is trained to make the generated displacement distribution close to FEA. The results show that the approximate distribution of displacement can be obtained by this model, and the calculation time is also lower than that of the FEA, which verifies the feasibility of solving displacement response by GAN. |
| format | Article |
| id | doaj-art-849ed39414294c08bb744ec6f76fa4d4 |
| institution | Kabale University |
| issn | 2096-5427 |
| language | zho |
| publishDate | 2019-01-01 |
| publisher | Editorial Office of Control and Information Technology |
| record_format | Article |
| series | Kongzhi Yu Xinxi Jishu |
| spelling | doaj-art-849ed39414294c08bb744ec6f76fa4d42025-08-25T06:52:05ZzhoEditorial Office of Control and Information TechnologyKongzhi Yu Xinxi Jishu2096-54272019-01-0136576282328294A Generative Adversarial Network Approach to EstimateFinite Element DisplacementWANG ZhangjunXU PingWANG ChunpengZHAO ZiliangCAI JunkunThe finite element analysis(FEA) usually requires complex procedures to set up, and depends on constitutive relation to obtain final simulation results. In order to explore solutions other than the finite element method, the displacement response is considered as a picture generation process with given conditions, bypassing the physical method. Based on generative adversarial network(GAN), and combined with DCGAN and CGAN, a deep learning model was proposed to directly obtain the displacement solution of two-dimensional plane instead of the finite element method. The proposed model is trained to make the generated displacement distribution close to FEA. The results show that the approximate distribution of displacement can be obtained by this model, and the calculation time is also lower than that of the FEA, which verifies the feasibility of solving displacement response by GAN.http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2019.06.300deep learninggenerative adversarial network(GAN)finite elementdisplacement solution |
| spellingShingle | WANG Zhangjun XU Ping WANG Chunpeng ZHAO Ziliang CAI Junkun A Generative Adversarial Network Approach to EstimateFinite Element Displacement Kongzhi Yu Xinxi Jishu deep learning generative adversarial network(GAN) finite element displacement solution |
| title | A Generative Adversarial Network Approach to EstimateFinite Element Displacement |
| title_full | A Generative Adversarial Network Approach to EstimateFinite Element Displacement |
| title_fullStr | A Generative Adversarial Network Approach to EstimateFinite Element Displacement |
| title_full_unstemmed | A Generative Adversarial Network Approach to EstimateFinite Element Displacement |
| title_short | A Generative Adversarial Network Approach to EstimateFinite Element Displacement |
| title_sort | generative adversarial network approach to estimatefinite element displacement |
| topic | deep learning generative adversarial network(GAN) finite element displacement solution |
| url | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2019.06.300 |
| work_keys_str_mv | AT wangzhangjun agenerativeadversarialnetworkapproachtoestimatefiniteelementdisplacement AT xuping agenerativeadversarialnetworkapproachtoestimatefiniteelementdisplacement AT wangchunpeng agenerativeadversarialnetworkapproachtoestimatefiniteelementdisplacement AT zhaoziliang agenerativeadversarialnetworkapproachtoestimatefiniteelementdisplacement AT caijunkun agenerativeadversarialnetworkapproachtoestimatefiniteelementdisplacement AT wangzhangjun generativeadversarialnetworkapproachtoestimatefiniteelementdisplacement AT xuping generativeadversarialnetworkapproachtoestimatefiniteelementdisplacement AT wangchunpeng generativeadversarialnetworkapproachtoestimatefiniteelementdisplacement AT zhaoziliang generativeadversarialnetworkapproachtoestimatefiniteelementdisplacement AT caijunkun generativeadversarialnetworkapproachtoestimatefiniteelementdisplacement |