In this work, we present a new approach based on metric learning for defining new similarity measures that are well-matched for design tasks in nanophotonics. Majority of the existing approaches use mean squared error (MSE) or mean absolute error (MAE) as the similarity measure to compare the desired and optimal spectra while it is clear that point-wise distance cannot capture the important features of the responses. Here, our goal is to use deep metric learning to provide a systematic approach for defining new metrics in nanophotonics.
This talk is focused on using the intelligent aspects of machine learning (ML) for both the understanding of the subtle properties of nanophotonic devices and their inverse design to achieve a desired response. It will be shown that by reducing the dimensionality of the problem using manifold learning techniques and simplifying the resulting networks using pruning, the computation complexity of the underlying artificial intelligence (AI) algorithms will be considerably reduced. Furthermore, by optimally defining the loss function (or the metric) for AI algorithms, priceless information about the properties of photonic nanostructures can be uncovered while facilitating the better visualization of the input-output relationship in these nanostructures. In addition, the resulting manifold-learning algorithms can be optimally trained to facilitate the inverse design of such nanostructures while minimizing the structural complexity. This talk will provide the foundation for both knowledge discovery and design in photonic nanostructures using manifold learning and metric learning and their application to the highly desired metaphotonic structures as an example platform.
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