Physics-informed neural networks for inverse problems in nano-optics and metamaterials

2 Dec 2019 Yuyao Chen Lu Lu George Em Karniadakis Luca Dal Negro

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... (read more)

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  • COMPUTATIONAL PHYSICS
  • OPTICS