Back-propagation optimization and multi-valued artificial neural networks for highly vivid structural color filter metasurfaces.

We introduce a novel technique for designing color filter metasurfaces using a data-driven approach based on deep learning. Our innovative approach employs inverse design principles to identify highly efficient designs that outperform all the configurations in the dataset, which consists of 585 distinct geometries solely. By combining Multi-Valued Artificial Neural Networks and back-propagation optimization, we overcome the limitations of previous approaches, such as poor performance due to extrapolation and undesired local minima.

High-Order Multimode Waveguide Interferometer for Optical Biosensing Applications.

A generalization of the concept of multimode interference sensors is presented here for the first time, to the best of our knowledge. The existing bimodal and trimodal sensors correspond to particular cases of those interference sensors. A thorough study of the properties of the multimode waveguide section provided a deeper insight into the behavior of this class of sensors, which allowed us to establish new criteria for designing more sensitive structures.

Superluminal X-shaped beams propagating without distortion along a coaxial guide.

In a previous paper we showed that localized superluminal solutions to the Maxwell equations exist, which propagate down (nonevanescence) regions of a metallic cylindrical waveguide. In this paper we construct analogous nondispersive waves propagating along coaxial cables. Such new solutions, in general, consist in trains of (undistorted) superluminal "X-shaped" pulses.