In this paper, we employ the emerging paradigm of physics-informed neural networks
(PINNs) for the solution of representative inverse scattering problems in photonic …

Neural operators have emerged as a powerful tool for solving partial differential equations in
the context of scientific machine learning. Here, we implement and train a modified Fourier …

Data inconsistency leads to a slow training process when deep neural networks are used for
the inverse design of photonic devices, an issue that arises from the fundamental property of …

In this work we investigate the use of deep inverse models (DIMs) for designing artificial
electromagnetic materials (AEMs)–such as metamaterials, photonic crystals, and …

Recent introduction of data-driven approaches based on deep-learning technology has
revolutionized the field of nanophotonics by allowing efficient inverse design methods. In …

Artificial intelligence is currently attracting unprecedented attention for its ability to tackle
hard problems with huge datasets, which have been rendered tractable by the giant …

The burgeoning research of graphene and other 2D materials enables many unprecedented
metamaterials and metadevices for applications on nanophotonics. The design of on …

Nanophotonic devices manipulate light at sub-wavelength scales, enabling tasks such as
light concentration, routing, and filtering. Designing these devices to achieve precise light …

In this paper, we develop a deep learning approach for the accurate solution of challenging
problems of near-field microscopy that leverages the powerful framework of physics …

Deep learning in the context of nano-photonics is mostly discussed in terms of its potential
for inverse design of photonic devices or nano-structures. Many of the recent works on …