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Published May 2020 | Version Published
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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.

Additional Information

© 2020 The Author(s).

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Eprint ID
105497
Resolver ID
CaltechAUTHORS:20200923-131223809

Related works

Is published in
https://ieeexplore.ieee.org/document/9192854 (URL)

Dates

Created
2020-09-23
Created from EPrint's datestamp field
Updated
2021-11-16
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