Machine learning for knowledge acquisition and accelerated inverse-design for non-Hermitian systems

Abstract

Machine learning has demonstrated effectiveness in optimizing complex physical structures. In this study, the authors employ a machine learning approach to inversely design non-Hermitian layered optical systems with gain and loss modulation, showing that the trained network can reveal the relation between asymmetric transmission and reflection spectra.