Parameter Space Compression and Random Structure Automatic Generation for the Inverse Design of Photonic Crystal Fibers Based on Convolutional Adversarial Autoencoder

An automatic iterative optimization and inverse design method for photonic crystal fiber (PCF) is proposed based on convolutional adversarial autoencoder (CAAE) and forward prediction convolutional neural network (PCNN). This method takes the two-dimensional (2D) material refractive index distributi...

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Vydáno v:Journal of lightwave technology Ročník 42; číslo 22; s. 7871 - 7881
Hlavní autoři: Yu, Haomiao, Huang, Wei, Xiao, Fangxin, Song, Binbin, Chen, Shengyong
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 15.11.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Artificial neural networks
Convolutional adversarial autoencoder
Cross-sections
Crystal fibers
Crystal structure
Design optimization
Inverse design
Lattice design
Lattices
Machine learning
Normal distribution
Optical fiber dispersion
Optical fiber networks
Optical fibers
Optical properties
Optical variables control
Optimization
Parameters
Photonic crystals
Random variables
Refractive index
Refractivity
structural optimization
Two dimensional materials
ISSN:0733-8724, 1558-2213
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Shrnutí:An automatic iterative optimization and inverse design method for photonic crystal fiber (PCF) is proposed based on convolutional adversarial autoencoder (CAAE) and forward prediction convolutional neural network (PCNN). This method takes the two-dimensional (2D) material refractive index distribution of the fiber cross-section as the structural parameter space and can achieve automatic generation and optimization of PCFs in the 2D parameter space. The automatic generation is based on CAAE, which can compress the 2D fiber structural parameter matrix into a 36-dimensional hyperparameter space with Gaussian distribution, and the Gaussian hyperparameters can be restored to the original 2D structural matrix through the decoder. The decoder can work independently and generate random PCFs after inputting Gaussian hyperparameters. Then, the forward PCNN is constructed to evaluate the optical property of the PCFs generated from the decoder. By combining the PCNN and CAAE networks, automatic generation and optimization of 2D PCF structures can be achieved. The structural variables generated and optimized are the refractive index distribution of the fiber cross-section, which is more flexible and can be applied to different types of PCFs. We also propose a transfer learning method for the random generation of different PCFs, which only needs a small amount of data to train the autoencoder, and efficient and accurate random generation of other PCFs with different lattice arrangements can be achieved. The proposed automatic generation and optimization method is flexible and efficient, which provide a new approach for the optimization and reverse design of PCF structures in 2D parameter space.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0733-8724
1558-2213
DOI:10.1109/JLT.2024.3364071