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 · 2024
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.