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Knowledge Discovery In Nanophotonics Using Geometric Deep Learning
More importantly, the one-class SVM algorithm can be trained to provide the degree of feasibility (or unfeasibility) of a response from a given nanostructure.
Deep Learning Reveals Underlying Physics of Light-matter Interactions in Nanophotonic Devices
In this paper, we present a deep learning-based (DL-based) algorithm, as a purely mathematical platform, for providing intuitive understanding of the properties of electromagnetic (EM) wave-matter interaction in nanostructures.
Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures
This approach reduces the computational complexity in solving both the forward problem (i. e., analysis) and the inverse problem (i. e., design) by orders of magnitude compared to conventional approaches.
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