Deep learning for the design of non-Hermitian topolectrical circuits
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917590879698944 |
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| author | Chen, Xi Sun, Jinyang Wang, Xiumei Jiang, Hengxuan Zhu, Dandan Zhou, Xingping |
| author_facet | Chen, Xi Sun, Jinyang Wang, Xiumei Jiang, Hengxuan Zhu, Dandan Zhou, Xingping |
| contents | Non-Hermitian topological phases can produce some remarkable properties, compared with their Hermitian counterpart, such as the breakdown of conventional bulk-boundary correspondence and the non-Hermitian topological edge mode. Here, we introduce several algorithms with multi-layer perceptron (MLP), and convolutional neural network (CNN) in the field of deep learning, to predict the winding of eigenvalues non-Hermitian Hamiltonians. Subsequently, we use the smallest module of the periodic circuit as one unit to construct high-dimensional circuit data features. Further, we use the Dense Convolutional Network (DenseNet), a type of convolutional neural network that utilizes dense connections between layers to design a non-Hermitian topolectrical Chern circuit, as the DenseNet algorithm is more suitable for processing high-dimensional data. Our results demonstrate the effectiveness of the deep learning network in capturing the global topological characteristics of a non-Hermitian system based on training data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09978 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep learning for the design of non-Hermitian topolectrical circuits Chen, Xi Sun, Jinyang Wang, Xiumei Jiang, Hengxuan Zhu, Dandan Zhou, Xingping Applied Physics Machine Learning Non-Hermitian topological phases can produce some remarkable properties, compared with their Hermitian counterpart, such as the breakdown of conventional bulk-boundary correspondence and the non-Hermitian topological edge mode. Here, we introduce several algorithms with multi-layer perceptron (MLP), and convolutional neural network (CNN) in the field of deep learning, to predict the winding of eigenvalues non-Hermitian Hamiltonians. Subsequently, we use the smallest module of the periodic circuit as one unit to construct high-dimensional circuit data features. Further, we use the Dense Convolutional Network (DenseNet), a type of convolutional neural network that utilizes dense connections between layers to design a non-Hermitian topolectrical Chern circuit, as the DenseNet algorithm is more suitable for processing high-dimensional data. Our results demonstrate the effectiveness of the deep learning network in capturing the global topological characteristics of a non-Hermitian system based on training data. |
| title | Deep learning for the design of non-Hermitian topolectrical circuits |
| topic | Applied Physics Machine Learning |
| url | https://arxiv.org/abs/2402.09978 |