Modified variational autoencoder for inversely predicting plasmonic nanofeatures for generating structural color

We apply a modified variational autoencoder (VAE) regressor for inversely retrieving the topological parameters of the building blocks of plasmonic composites for generating structural colors as per requirement. We demonstrate results of a comparison study between inverse models based on generative VAEs as well as conventional tandem networks that have been favored traditionally. We describe our strategy for improving the performance of our model by filtering the simulated dataset prior to training. The VAE- based inverse model links the electromagnetic response expressed as the structural color to the geometrical dimensions from the latent space using a multilayer perceptron regressor and shows better accuracy over a conventional tandem inverse model.