The growing maturity of nanofabrication has ushered massive sophisticated optical
structures available on a photonic chip. The integration of subwavelength-structured …

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 …

Transparent radiative coolers can be used as window materials to reduce cooling energy
needs for buildings and automobiles, which may contribute significantly to addressing …

Wavelength-selective thermal emitters (WS-TEs) have been frequently designed to achieve
desired target emissivity spectra, as a typical emissivity engineering, for broad applications …

Photonic inverse design concerns the problem of finding photonic structures with target
optical properties. However, traditional methods based on optimization algorithms are time …

Recent years have witnessed a rapid development in the field of structural coloration, colors
generated from the interaction of nanostructures with light. Compared to conventional color …

Nanophotonic devices have enabled microscopic control of light with an unprecedented
spatial resolution by employing subwavelength optical elements that can strongly interact …

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

Deep learning has been revolutionizing information processing in many fields of science
and engineering owing to the massively growing amounts of data and the advances in deep …