Deep learning for the design of non-Hermitian topolectrical circuits

arXiv:2402.09978v1 Announce Type: cross
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, …

abstract algorithms arxiv breakdown bulk cnn convolutional neural network cs.lg deep learning design edge layer mlp network neural network perceptron physics.app-ph type