  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.
