In this paper we employ the emerging paradigm of physics-informed neural
networks (PINNs) for the solution of representative inverse scattering problems
in photonic metamaterials and nano-optics technologies. In particular, we
successfully apply mesh-free PINNs to the difficult task of retrieving the
effective permittivity parameters of a number of finite-size scattering systems
that involve many interacting nanostructures as well as multi-component
nanoparticles. Our methodology is fully validated by numerical simulations
based on the Finite Element Method (FEM). The development of physics-informed
deep learning techniques for inverse scattering can enable the design of novel
functional nanostructures and significantly broaden the design space of
metamaterials by naturally accounting for radiation and finite-size effects
beyond the limitations of traditional effective medium theories.

本文介绍如何应用物理信息神经网络（PINNs）求解光子超材料和纳米光学技术中的逆散射问题，并成功应用于多组分纳米粒子等多种散射系统的介电常数参数反演，从而拓展超材料的设计空间和功能。