Artificial neural networks based optimization techniques: A review
In the last few years, intensive research has been done to enhance artificial intelligence (AI)
using optimization techniques. In this paper, we present an extensive review of artificial …
using optimization techniques. In this paper, we present an extensive review of artificial …
Deep learning for the design of photonic structures
Innovative approaches and tools play an important role in sha** design, characterization
and optimization for the field of photonics. As a subset of machine learning that learns …
and optimization for the field of photonics. As a subset of machine learning that learns …
Deep neural networks for the evaluation and design of photonic devices
The data-science revolution is poised to transform the way photonic systems are simulated
and designed. Photonic systems are, in many ways, an ideal substrate for machine learning …
and designed. Photonic systems are, in many ways, an ideal substrate for machine learning …
Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi‐supervised learning strategy
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 …
in a prescribed manner using delicately designed subwavelength structures, so‐called meta …
Deep learning the electromagnetic properties of metamaterials—a comprehensive review
Deep neural networks (DNNs) are empirically derived systems that have transformed
traditional research methods, and are driving scientific discovery. Artificial electromagnetic …
traditional research methods, and are driving scientific discovery. Artificial electromagnetic …
Tackling photonic inverse design with machine learning
Abstract Machine learning, as a study of algorithms that automate prediction and decision‐
making based on complex data, has become one of the most effective tools in the study of …
making based on complex data, has become one of the most effective tools in the study of …
Machine learning for nanoplasmonics
Plasmonic nanomaterials have outstanding optoelectronic properties potentially enabling
the next generation of catalysts, sensors, lasers and photothermal devices. Owing to optical …
the next generation of catalysts, sensors, lasers and photothermal devices. Owing to optical …
Machine learning-driven new material discovery
J Cai, X Chu, K Xu, H Li, J Wei - Nanoscale Advances, 2020 - pubs.rsc.org
New materials can bring about tremendous progress in technology and applications.
However, the commonly used trial-and-error method cannot meet the current need for new …
However, the commonly used trial-and-error method cannot meet the current need for new …
Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures
In this paper, we demonstrate a computationally efficient new approach based on deep
learning (DL) techniques for analysis, design and optimization of electromagnetic (EM) …
learning (DL) techniques for analysis, design and optimization of electromagnetic (EM) …
Deep learning: a new tool for photonic nanostructure design
RS Hegde - Nanoscale Advances, 2020 - pubs.rsc.org
Early results have shown the potential of Deep Learning (DL) to disrupt the fields of optical
inverse-design, particularly, the inverse design of nanostructures. In the last three years, the …
inverse-design, particularly, the inverse design of nanostructures. In the last three years, the …