Published May 2020
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Mirrored Plasmonic Filter Design via Active Learning of Multi-Fidelity Physical Models
Chicago
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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.
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© 2020 The Author(s).Attached Files
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- Eprint ID
- 105497
- Resolver ID
- CaltechAUTHORS:20200923-131223809
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2020-09-23Created from EPrint's datestamp field
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2021-11-16Created from EPrint's last_modified field