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.

翻译：非厄米拓扑相相较于其厄米对应物，能产生一些显著特性，例如传统体-边对应关系的失效以及非厄米拓扑边缘模式。本文引入了几种基于多层感知器（MLP）和卷积神经网络（CNN）的深度学习算法，用于预测非厄米哈密顿量本征值的缠绕数。随后，我们以周期电路的最小模块为单元，构建高维电路数据特征。进一步，我们采用密集连接卷积网络（DenseNet），一种利用层间密集连接的卷积神经网络，来设计非厄米拓扑电路陈绝缘体，因为DenseNet算法更适合处理高维数据。我们的研究结果表明，深度学习网络能够基于训练数据有效捕捉非厄米系统的全局拓扑特性。