Physics-informed neural networks for inverse problems in nano-optics and metamaterials
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Apr 06, 2020
- Source ID
- 10.1364/oe.384875
Entities
People
- George Karniadakis
- Lu Lu
- Luca Dal Negro
- Yuyao Chen
Organizations
- United States Army Research Laboratory
- United States Department of Energy