Generative model for multiple-purpose inverse design and forward prediction of disordered waveguides in linear and nonlinear regimes

Ziheng Guo; Zhongliang Guo; Oggie Arandelovic; Andrea di Falco

doi:10.1117/12.3017130

18 June 2024 Generative model for multiple-purpose inverse design and forward prediction of disordered waveguides in linear and nonlinear regimes

Ziheng Guo, Zhongliang Guo, Oggie Arandelovic, Andrea di Falco

Author Affiliations +

Proceedings Volume 13017, Machine Learning in Photonics; 1301702 (2024) https://doi.org/10.1117/12.3017130
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

Data-driven machine learning framework has become a state-of-art technique to explore whole parameters design space for designing complex systems. In this work, we used conditional generative adversarial networks to inverse design three problems that we are interested in random nanophotonic systems: pattern optimization, geometry generation, and pattern reproduction. Meanwhile, automation convolutional neural networks group for forward prediction of the transmission spectra of disordered waveguides in linear and nonlinear regimes, at telecommunication wavelengths.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ziheng Guo, Zhongliang Guo, Oggie Arandelovic, and Andrea di Falco "Generative model for multiple-purpose inverse design and forward prediction of disordered waveguides in linear and nonlinear regimes", Proc. SPIE 13017, Machine Learning in Photonics, 1301702 (18 June 2024); https://doi.org/10.1117/12.3017130

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