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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Published in:	Journal of lightwave technology Vol. 42; no. 22; pp. 7871 - 7881
Main Authors:	Yu, Haomiao, Huang, Wei, Xiao, Fangxin, Song, Binbin, Chen, Shengyong
Format:	Journal Article
Language:	English
Published:	New York IEEE 15.11.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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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Abstract	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.
AbstractList	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.
Author	Yu, Haomiao Chen, Shengyong Song, Binbin Xiao, Fangxin Huang, Wei
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SubjectTerms	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
Title	Parameter Space Compression and Random Structure Automatic Generation for the Inverse Design of Photonic Crystal Fibers Based on Convolutional Adversarial Autoencoder
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