Non Hermitian

Non-Hermitian systems, characterized by Hamiltonians with complex eigenvalues, are a burgeoning area of research exploring physical phenomena beyond the limitations of traditional Hermitian physics. Current investigations focus on leveraging machine learning, particularly convolutional neural networks and multi-layer perceptrons, to efficiently identify topological phases, predict eigenvalue winding numbers, and accelerate the inverse design of these systems, for example in topolectrical circuits. This work is significant because it allows for a deeper understanding of non-Hermitian phenomena and facilitates the design of novel materials and devices with tailored properties, such as asymmetric reflection and transmission.