Machine learning for knowledge acquisition and accelerated inverse-design for non-Hermitian systems. (arXiv:2204.13376v1 [physics.optics])

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 …

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