New machine learning paradigms for knowledge discovery and inverse design of photonic nanostructures

Machine-learning algorithms are powerful tools in developing reliable models to relate the design space of a nanophotonic structure to its response space. They can be used not only to simplify the inverse design problem but also to provide valuable insight about the physics of light-matter interaction. This talk will provide a new approach through combining manifold-learning algorithms for reducing the dimensionality of the problem with metric-learning techniques for more insightful mapping of the input-output relation to the dimensionality-reduced (or the latent) space. In addition to covering the fundamental properties of the presented algorithms, their applications to both the inverse design and the knowledge discovery in state-of-the-art metaphotonic structures will be discussed.