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

TL;DR

This paper employs machine learning techniques to accelerate the inverse design process and enhance understanding of non-Hermitian systems by relating spectral responses to physical parameters.

Contribution

It introduces a deep learning framework that predicts spectral responses and recognizes non-Hermitian features, advancing inverse design and physical insight in non-Hermitian physics.

Findings

Deep learning models relate transmission and asymmetric reflection.

Sub-manifold learning identifies non-Hermitian features.

Framework predicts spectral response feasibility.

Abstract

Non-Hermitian systems offer new platforms for unusual physical properties that can be flexibly manipulated by redistribution of the real and imaginary parts of refractive indices, whose presence breaks conventional wave propagation symmetries, leading to asymmetric reflection and symmetric transmission with respect to the wave propagation direction. Here, we use supervised and unsupervised learning techniques for knowledge acquisition in non-Hermitian systems which accelerate the inverse design process. In particular, we construct a deep learning model that relates the transmission and asymmetric reflection in non-conservative settings and proposes sub-manifold learning to recognize non-Hermitian features from transmission spectra. The developed deep learning framework determines the feasibility of a desired spectral response for a given structure and uncovers the role of effective gain-loss parameters to tailor the spectral response. These findings pave the way for intelligent inverse design and shape our understanding of the physical mechanism in general non-Hermitian systems.