Manifold learning: A promising tool for knowledge discovery and inverse design in nanophotonics

Abstract

Here, we present a new approach based on manifold learning for inverse design and knowledge discovery in nanophotonics. We present the unique capabilities of manifold learning approaches for reducing the dimensionality of the high-dimensional relationships in photonic nanostructures. We show how this can help to understand the underlying patterns in the responses of such nanostructures. Such a visualization in the low-dimensional space enables knowledge discovery and studying the underlying physics of nanostructures and can facilitate the inverse design. We also use this method to study the role of the design parameters and design a class of nanostructure while reducing the design complexity.