Deep learning for the design of non-Hermitian topolectrical circuits

• URL: http://arxiv.org/abs/2402.09978v1
• Date: Thu, 15 Feb 2024 14:41:55 GMT
• Title: Deep learning for the design of non-Hermitian topolectrical circuits
• Authors: Xi Chen, Jinyang Sun, Xiumei Wang, Hengxuan Jiang, Dandan Zhu, and Xingping Zhou
• Abstract summary: 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. 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.
• Score: 8.960003862907877
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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