Deep learning for the design of non-Hermitian topolectrical circuits
<|reference_start|>Deep learning for the design of non-Hermitian topolectrical circuits: 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.<|reference_end|>
@article{chen2024deep,
    title={Deep learning for the design of non-Hermitian topolectrical circuits},
    author={Xi Chen, Jinyang Sun, Xiumei Wang, Hengxuan Jiang, Dandan Zhu, and
  Xingping Zhou},
    journal={arXiv preprint arXiv:2402.09978},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.09978},
    primaryClass={physics.app-ph cs.LG}
}