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

• Published in: Optics express Vol. 28; no. 8; pp. 11618 - 11633
• Main Authors: Chen, Yuyao, Lu, Lu, Karniadakis, George Em, Dal Negro, Luca
• Format: Journal Article
• Language: English
• Published: United States Optical Society of America 13-04-2020
• Subjects: CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; Optics
• ISSN: 1094-4087; 1094-4087
• DOI: 10.1364/oe.384875

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.