Bidirectional Adversarial Autoencoders for the design of Plasmonic Metasurfaces

arXiv:2405.04056v1 Announce Type: cross
Abstract: Deep Learning has been a critical part of designing inverse design methods that are computationally efficient and accurate. An example of this is the design of photonic metasurfaces by using their photoluminescent spectrum as the input data to predict their topology. One fundamental challenge of these systems is their ability to represent nonlinear relationships between sets of data that have different dimensionalities. Existing design methods often implement a conditional Generative Adversarial Network in order to …

abstract adversarial arxiv autoencoders challenge cs.lg data deep learning design designing example fundamental part physics.optics spectrum topology type