Deep learning for the design of photonic structures

W Ma, Z Liu, ZA Kudyshev, A Boltasseva, W Cai… - Nature Photonics, 2021 - nature.com
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 nano-photonics: inverse design and beyond

PR Wiecha, A Arbouet, C Girard, OL Muskens - Photonics Research, 2021 - opg.optica.org
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-enabled on-demand design of chiral metamaterials

W Ma, F Cheng, Y Liu - ACS nano, 2018 - ACS Publications
Deep-learning framework has significantly impelled the development of modern machine
learning technology by continuously pushing the limit of traditional recognition and …

Generative model for the inverse design of metasurfaces

Z Liu, D Zhu, SP Rodrigues, KT Lee, W Cai - Nano letters, 2018 - ACS Publications
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 …

Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi‐supervised learning strategy

W Ma, F Cheng, Y Xu, Q Wen, Y Liu - Advanced Materials, 2019 - Wiley Online Library
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 …

[HTML][HTML] Phase-to-pattern inverse design paradigm for fast realization of functional metasurfaces via transfer learning

R Zhu, T Qiu, J Wang, S Sui, C Hao, T Liu, Y Li… - Nature …, 2021 - nature.com
Metasurfaces have provided unprecedented freedom for manipulating electromagnetic
waves. In metasurface design, massive meta-atoms have to be optimized to produce the …

Training deep neural networks for the inverse design of nanophotonic structures

D Liu, Y Tan, E Khoram, Z Yu - Acs Photonics, 2018 - ACS Publications
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 for accelerated all-dielectric metasurface design

CC Nadell, B Huang, JM Malof, WJ Padilla - Optics express, 2019 - opg.optica.org
Deep learning has risen to the forefront of many fields in recent years, overcoming
challenges previously considered intractable with conventional means. Materials discovery …

Deep learning the electromagnetic properties of metamaterials—a comprehensive review

O Khatib, S Ren, J Malof… - Advanced Functional …, 2021 - Wiley Online Library
Deep neural networks (DNNs) are empirically derived systems that have transformed
traditional research methods, and are driving scientific discovery. Artificial electromagnetic …

Weighting factor design in model predictive control of power electronic converters: An artificial neural network approach

T Dragičević, M Novak - IEEE Transactions on Industrial …, 2018 - ieeexplore.ieee.org
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 …