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Mirrored Plasmonic Filter Design via Active Learning of Multi-Fidelity Physical Models

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Abstract

We designed mirrored plasmonic filters using an advanced active machine learning algorithm that efficiently explores multiple physical models with different approximation fidelities and costs. This method is applicable to a variety of nanophotonics optimization problems.

© 2020 The Author(s)

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Poster Presentation

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