Innovative approaches and tools play an important role in shaping design, characterization
and optimization for the field of photonics. As a subset of machine learning that learns …

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-learning framework has significantly impelled the development of modern machine
learning technology by continuously pushing the limit of traditional recognition and …

The advent of metasurfaces in recent years has ushered in a revolutionary means to
manipulate the behavior of light on the nanoscale. The design of such structures, to date …

The research of metamaterials has achieved enormous success in the manipulation of light
in a prescribed manner using delicately designed subwavelength structures, so‐called meta …

Metasurfaces have provided unprecedented freedom for manipulating electromagnetic
waves. In metasurface design, massive meta-atoms have to be optimized to produce the …

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 …

Deep learning has risen to the forefront of many fields in recent years, overcoming
challenges previously considered intractable with conventional means. Materials discovery …

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

This paper proposes the use of an artificial neural network (ANN) for solving one of the
ongoing research challenges in finite set-model predictive control (FSMPC) of power …