Deep learning for the design of non-Hermitian topolectrical circuits
Xi Chen Jinyang Sun Xiumei Wang Hengxuan Jiang Dandan Zhu Xingping Zhou
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.