Analysis of nonlinear multi-field coupling responses of piezoelectric semiconductor rods via machine learning
ABSTRACTPiezoelectric semiconductors (PSs) have widespread applications in semiconductor devices due to the coexistence of piezoelectricity and semiconducting properties. It is very important to conduct a theoretical analysis of PS structures. However, the present of nonlinearity in the partial diff...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-01-01
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| Series: | International Journal of Smart and Nano Materials |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19475411.2023.2282780 |
| _version_ | 1827325444254334976 |
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| author | Chuwei Wu Zhengguang Xiao Yuting Guo Chunli Zhang |
| author_facet | Chuwei Wu Zhengguang Xiao Yuting Guo Chunli Zhang |
| author_sort | Chuwei Wu |
| collection | DOAJ |
| description | ABSTRACTPiezoelectric semiconductors (PSs) have widespread applications in semiconductor devices due to the coexistence of piezoelectricity and semiconducting properties. It is very important to conduct a theoretical analysis of PS structures. However, the present of nonlinearity in the partial differential equations (PDEs) that describe those multi-field coupling mechanical behaviors of PSs poses a significant mathematical challenge when studying these PS structures. In this paper, we present a novel approach based on machine learning for solving multi-field coupling problems in PS structures. A physics-informed neural networks (PINNs) is constructed for predicting the multi-field coupling behaviors of PS rods with extensional deformation. By utilizing the proposed PINNs, we evaluate the multi-field coupling responses of a ZnO rod under static and dynamic axial forces. Numerical results demonstrate that the proposed PINNs exhibit high accuracy in solving both static and dynamic problems associated with PS structures. It provides an effective approach to predicting the nonlinear multi-field coupling phenomena in PS structures. |
| first_indexed | 2024-03-07T14:27:17Z |
| format | Article |
| id | doaj.art-9aad21f06e8f464dafa82c47fff0d309 |
| institution | Directory Open Access Journal |
| issn | 1947-5411 1947-542X |
| language | English |
| last_indexed | 2024-03-07T14:27:17Z |
| publishDate | 2024-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Smart and Nano Materials |
| spelling | doaj.art-9aad21f06e8f464dafa82c47fff0d3092024-03-06T06:05:48ZengTaylor & Francis GroupInternational Journal of Smart and Nano Materials1947-54111947-542X2024-01-01151627410.1080/19475411.2023.2282780Analysis of nonlinear multi-field coupling responses of piezoelectric semiconductor rods via machine learningChuwei Wu0Zhengguang Xiao1Yuting Guo2Chunli Zhang3Department of Engineering Mechanics, Zhejiang University, Hangzhou, Zhejiang, ChinaDepartment of Engineering Mechanics, Zhejiang University, Hangzhou, Zhejiang, ChinaDepartment of Engineering Mechanics, Zhejiang University, Hangzhou, Zhejiang, ChinaDepartment of Engineering Mechanics, Zhejiang University, Hangzhou, Zhejiang, ChinaABSTRACTPiezoelectric semiconductors (PSs) have widespread applications in semiconductor devices due to the coexistence of piezoelectricity and semiconducting properties. It is very important to conduct a theoretical analysis of PS structures. However, the present of nonlinearity in the partial differential equations (PDEs) that describe those multi-field coupling mechanical behaviors of PSs poses a significant mathematical challenge when studying these PS structures. In this paper, we present a novel approach based on machine learning for solving multi-field coupling problems in PS structures. A physics-informed neural networks (PINNs) is constructed for predicting the multi-field coupling behaviors of PS rods with extensional deformation. By utilizing the proposed PINNs, we evaluate the multi-field coupling responses of a ZnO rod under static and dynamic axial forces. Numerical results demonstrate that the proposed PINNs exhibit high accuracy in solving both static and dynamic problems associated with PS structures. It provides an effective approach to predicting the nonlinear multi-field coupling phenomena in PS structures.https://www.tandfonline.com/doi/10.1080/19475411.2023.2282780PS rodmulti-field coupling responsemachine learningPINNs |
| spellingShingle | Chuwei Wu Zhengguang Xiao Yuting Guo Chunli Zhang Analysis of nonlinear multi-field coupling responses of piezoelectric semiconductor rods via machine learning International Journal of Smart and Nano Materials PS rod multi-field coupling response machine learning PINNs |
| title | Analysis of nonlinear multi-field coupling responses of piezoelectric semiconductor rods via machine learning |
| title_full | Analysis of nonlinear multi-field coupling responses of piezoelectric semiconductor rods via machine learning |
| title_fullStr | Analysis of nonlinear multi-field coupling responses of piezoelectric semiconductor rods via machine learning |
| title_full_unstemmed | Analysis of nonlinear multi-field coupling responses of piezoelectric semiconductor rods via machine learning |
| title_short | Analysis of nonlinear multi-field coupling responses of piezoelectric semiconductor rods via machine learning |
| title_sort | analysis of nonlinear multi field coupling responses of piezoelectric semiconductor rods via machine learning |
| topic | PS rod multi-field coupling response machine learning PINNs |
| url | https://www.tandfonline.com/doi/10.1080/19475411.2023.2282780 |
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