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Non-Hermitian systems offer new platforms for unusual physical propertiesthat can be flexibly manipulated by redistribution of the real and imaginaryparts of refractive indices, whose presence breaks conventional wavepropagation symmetries, leading to asymmetric reflection and symmetrictransmission with respect to the wave propagation direction. Here, we usesupervised and unsupervised learning techniques for knowledge acquisition innon-Hermitian systems which accelerate the inverse design process. Inparticular, we construct a deep learning model that relates the transmissionand asymmetric reflection in non-conservative settings and proposessub-manifold learning to recognize non-Hermitian features from transmissionspectra. The developed deep learning framework determines the feasibility of adesired spectral response for a given structure and uncovers the role ofeffective gain-loss parameters to tailor the spectral response. These findingspave the way for intelligent inverse design and shape our understanding of thephysical mechanism in general non-Hermitian systems.

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