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- W4386275947 abstract "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." @default.
- W4386275947 created "2023-08-31" @default.
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- W4386275947 date "2023-03-15" @default.
- W4386275947 modified "2023-09-29" @default.
- W4386275947 title "New paradigms in manifold learning for knowledge discovery and inverse design of photonic nanostructures" @default.
- W4386275947 doi "https://doi.org/10.1117/12.2662754" @default.
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