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Bayesian Optimization for Nested Adversarial Variational Autoencoder in Tunable Nanophotonic Device Design

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Abstract

We propose a device design framework based on Bayesian optimization for efficient latent sampling of adversarial generative neural networks to expedite inverse design of tunable nanophotonic wavelength splitters. The resulting design operates at broadband telecomm-wavelengths and is electrically switchable via liquid crystal tuning.

© 2023 The Author(s)

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