[PDF][PDF] Intelligent metaphotonics empowered by machine learning
In the recent years, a dramatic boost of the research is observed at the junction of photonics,
machine learning and artificial intelligence. A new methodology can be applied to the …
machine learning and artificial intelligence. A new methodology can be applied to the …
[PDF][PDF] Direct field-to-pattern monolithic design of holographic metasurface via residual encoder-decoder convolutional neural network
R Zhu, J Wang, T Qiu, D Yang, B Feng… - Opto-Electronic …, 2023 - researching.cn
Complex-amplitude holographic metasurfaces (CAHMs) with the flexibility in modulating
phase and amplitude profiles have been used to manipulate the propagation of wavefront …
phase and amplitude profiles have been used to manipulate the propagation of wavefront …
Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications
Exponential growth in data rate demands has driven efforts to develop novel beamforming
techniques for realizing massive multiple-input and multiple-output (MIMO) systems in sixth …
techniques for realizing massive multiple-input and multiple-output (MIMO) systems in sixth …
A deep neural network for general scattering matrix
The scattering matrix is the mathematical representation of the scattering characteristics of
any scatterer. Nevertheless, except for scatterers with high symmetry like spheres or …
any scatterer. Nevertheless, except for scatterers with high symmetry like spheres or …
Data-driven design of multilayer hyperbolic metamaterials for near-field thermal radiative modulator with high modulation contrast
The thermal modulator based on the near-field radiative heat transfer has wide applications
in thermoelectric diodes, thermoelectric transistors, and thermal storage. However, the …
in thermoelectric diodes, thermoelectric transistors, and thermal storage. However, the …
Machine-learning-empowered multispectral metafilm with reduced radar cross section, low infrared emissivity, and visible transparency
R Zhu, J Wang, J Jiang, C Xu, C Liu, Y Jia, S Sui… - Photonics …, 2022 - opg.optica.org
For camouflage applications, the performance requirements for metamaterials in different
electromagnetic spectra are usually contradictory, which makes it difficult to develop …
electromagnetic spectra are usually contradictory, which makes it difficult to develop …
[HTML][HTML] Vectorial-holography metasurface empowered by orthogonality-simplified machine learning
R Zhu, J Wang, C Ding, Y Li, Z Chu, X Wang, T Liu… - Materials & Design, 2022 - Elsevier
Metasurfaces can provide unprecedented degree of freedom in manipulating
electromagnetic waves and have been introduced to holography. Aiming to explore the full …
electromagnetic waves and have been introduced to holography. Aiming to explore the full …
Inverse design of electromagnetic metamaterials: from iterative to deep learning-based methods
In recent years, considerable research advancements have emerged in the application of
inverse design methods to enhance the performance of electromagnetic (EM) …
inverse design methods to enhance the performance of electromagnetic (EM) …
Deep learning in photonics: Introduction
The connection between Maxwell's equations and neural networks opens unprecedented
opportunities at the interface between photonics and deep learning. This feature issue …
opportunities at the interface between photonics and deep learning. This feature issue …
Long short-term memory neural network for directly inverse design of nanofin metasurface
W Deng, Z Xu, J Wang, J Lv - Optics Letters, 2022 - opg.optica.org
In this Letter, the neural network long short-term memory (LSTM) is used to quickly and
accurately predict the polarization sensitivity of a nanofin metasurface. In the forward …
accurately predict the polarization sensitivity of a nanofin metasurface. In the forward …