Machine learning for knowledge acquisition and accelerated inverse-design for non-Hermitian systems
This work addresses the challenge of designing non-Hermitian systems with specific spectral properties, which is important for researchers in photonics and wave physics, though it appears incremental in applying existing ML techniques to this domain.
The researchers tackled the problem of inverse design for non-Hermitian systems by developing a deep learning framework that relates transmission and asymmetric reflection to accelerate the design process, uncovering the role of effective gain-loss parameters to tailor spectral responses.
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.