5 Papers
8 Citations
Lu Wang is an academic researcher from Huazhong University of Science and Technology. The author has contributed to research in topics: Surface plasmon resonance & Deep learning. The author has an hindex of 1, co-authored 5 publications.
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Papers
Design of high-performance plasmonic nanosensors by particle swarm optimization algorithm combined with machine learning.
TL;DR: An algorithm based on particle swarm optimization (PSO), which in combination with a machine learning (ML) model, is used to design plasmonic sensors and is expected to pave the way for the design of nanophotonic devices in future.
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Efficient inverse design and spectrum prediction for nanophotonic devices based on deep recurrent neural networks
Ruoqin Yan,Tao Wang,Xiaoyun Jiang,Qingfang Zhong,Xing Huang,Lu Wang,Xinzhao Yue,Huimin Wang,Yuandong Wang +8 more
TL;DR: A deep learning method based on an improved recurrent neural network to extract the sequence characteristics of a spectrum and achieve inverse design and spectrum prediction and is capable of predicting an unknown spectrum based on a known spectrum with only 0.32% mean relative error.
14
Ultra-narrowband near-infrared tunable two-dimensional perfect absorber for refractive index sensing.
TL;DR: In this article, an ultra-narrow-bandwidth near-infrared perfect plasmonic absorber with a periodic structure composed of metal-insulator-metal configuration is numerically designed and analyzed for a refractive index sensor.
6
Design and Numerical Analysis of Extreme Sensitivity Refractive Index Sensor Based on Cavity Resonance Mode With Strong Electric Field
TL;DR: In this paper, the authors proposed a less sophisticated sensing platform based on a thin metal film resonance cavity, which can reach 1,456,700 nm/RIU and 1,234,500 /RIU, respectively.
3
Efficient inverse design and spectrum prediction for nanophotonic devices based on deep recurrent neural networks.
TL;DR: In this article, a deep learning method based on an improved recurrent neural network was proposed to extract the sequence characteristics of a spectrum and achieve inverse design and spectrum prediction in nanophotonics.