Design by neural network of concentric multilayered cylindrical metamaterials. (18th March 2020) - Akashi, Naoto; Toma, Mana; Kajikawa, Kotaro
Abstract: Artificial neural networks (NNs) that have deeply learned the optical responses from metamaterials can predict the optical spectra from a given metamaterial without solving Maxwell's equations. Prediction is extremely fast because of the low computational complexity. We report here two inverse designs of concentric multilayered cylinder metamaterials, using trained NN models, and discuss the accuracy of the prediction. We also predict a cloaking condition for invisibility having performance better than that derived by transformation optics, as a further application of NNs to metamaterial design.