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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Veröffentlicht in:	Journal of lightwave technology Jg. 42; H. 22; S. 7871 - 7881
Hauptverfasser:	Yu, Haomiao, Huang, Wei, Xiao, Fangxin, Song, Binbin, Chen, Shengyong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 15.11.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
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