scholarly journals Inverse design of metasurface optical filters using deep neural network with high degrees of freedom

InfoMat ◽  
2020 ◽  
Author(s):  
Xiao Han ◽  
Ziyang Fan ◽  
Zeyang Liu ◽  
Chao Li ◽  
L. Jay Guo
Keyword(s):  
Neural Network ◽  
Degrees Of Freedom ◽  
Deep Neural Network ◽  
Optical Filters ◽  
Inverse Design

Inverse design for integrated photonics using deep neural network

2021 ◽  
Author(s):  
Keisuke Kojima ◽  
Toshiaki Koike-Akino ◽  
Yingheng Tang ◽  
Ye Wang
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Inverse Design ◽  
Integrated Photonics

scholarly journals Deep Neural Network Inverse Design of Integrated Photonic Power Splitters

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Mohammad H. Tahersima ◽  
Keisuke Kojima ◽  
Toshiaki Koike-Akino ◽  
Devesh Jha ◽  
Bingnan Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Inverse Design ◽  
Power Splitters

Deep Neural Network Inverse-Design for Long Wave Infrared Hyperspectral Imaging

2020 ◽  
Author(s):  
Clayton Fowler ◽  
Sensong An ◽  
Bowen Zheng ◽  
Hong Tang ◽  
Hang Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Hyperspectral Imaging ◽  
Deep Neural Network ◽  
Inverse Design ◽  
Long Wave ◽  
Long Wave Infrared

Inverse design of a Helmholtz resonator based low-frequency acoustic absorber using deep neural network

2021 ◽  
Vol 129 (17) ◽  
pp. 174901
Author(s):  
K. Mahesh ◽  
S. Kumar Ranjith ◽  
R. S. Mini
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Low Frequency ◽  
Helmholtz Resonator ◽  
Inverse Design

Inverse Design of Graphene FET by Deep Neural Network

2021 ◽  
Author(s):  
Gyeong Min Seo ◽  
Chang-Ki Baek ◽  
Byoung Don Kong
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Inverse Design ◽  
Graphene Fet

Analysis of Deep Neural Network Models for Inverse Design of Silicon Photonic Grating Coupler

2021 ◽  
Vol 39 (9) ◽  
pp. 2790-2799 ◽  
Author(s):  
Xin Tu ◽  
Wansheng Xie ◽  
Zhenmin Chen ◽  
Ming-Feng Ge ◽  
Tianye Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Network Models ◽  
Inverse Design ◽  
Grating Coupler ◽  
Neural Network Models ◽  
Silicon Photonic

scholarly journals Deep neural network-based automatic metasurface design with a wide frequency range

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fardin Ghorbani ◽  
Sina Beyraghi ◽  
Javad Shabanpour ◽  
Homayoon Oraizi ◽  
Hossein Soleimani ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Cell Structure ◽  
Machine Learning Algorithms ◽  
Inverse Design ◽  
Computational Time ◽  
Network Architectures ◽  
Average Accuracy ◽  
Wide Range ◽  
Working Frequency

AbstractBeyond the scope of conventional metasurface, which necessitates plenty of computational resources and time, an inverse design approach using machine learning algorithms promises an effective way for metasurface design. In this paper, benefiting from Deep Neural Network (DNN), an inverse design procedure of a metasurface in an ultra-wide working frequency band is presented in which the output unit cell structure can be directly computed by a specified design target. To reach the highest working frequency for training the DNN, we consider 8 ring-shaped patterns to generate resonant notches at a wide range of working frequencies from 4 to 45 GHz. We propose two network architectures. In one architecture, we restrict the output of the DNN, so the network can only generate the metasurface structure from the input of 8 ring-shaped patterns. This approach drastically reduces the computational time, while keeping the network’s accuracy above 91%. We show that our model based on DNN can satisfactorily generate the output metasurface structure with an average accuracy of over 90% in both network architectures. Determination of the metasurface structure directly without time-consuming optimization procedures, an ultra-wide working frequency, and high average accuracy equip an inspiring platform for engineering projects without the need for complex electromagnetic theory.

Study on Human Detection System Using Deep Neural Network and Alternative Learning for Autonomous Flying Drones

2019 ◽  
Vol 139 (2) ◽  
pp. 149-157
Author(s):  
Itaru Nagayama ◽  
Wakaki Uehara ◽  
Takaya Miyazato
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Detection System ◽  
Human Detection ◽  
Alternative Learning

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification
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