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 …

Deep neural networks (DNNs) are empirically derived systems that have transformed
traditional research methods, and are driving scientific discovery. Artificial electromagnetic …

Here we present a roadmap on Photonic metasurfaces. This document consists of a number
of perspective articles on different applications, challenge areas or technologies underlying …

Based on the demand for ideal selective solar absorbers to harvest a full solar spectrum, we
present an auspicious method, the multi-island genetic algorithm (MIGA), to optimize the …

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

Here, we present a new approach based on manifold learning for knowledge discovery and
inverse design with minimal complexity in photonic nanostructures. Our approach builds on …

Image visual quality is of fundamental importance for three-dimensional (3D) light-field
displays. The pixels of a light-field display are enlarged after the imaging of the light-field …

Optical metasurfaces, planar artificial media capable of controlling light propagation, are
transitioning from laboratory curiosity to commercial applications. This shift requires …

Many phenomena in physics, including light, water waves, and sound, are described by
wave equations. Given their coefficients, wave equations can be solved to high accuracy …

Recently, the metasurfaces for independently controlling the wavefront and amplitude of two
orthogonal circularly polarized electromagnetic (EM) waves have been demonstrated to …