Neural Network Method for Dielectric Optical Coating Design
We use neural networks to address the challenge of deriving dielectric coating designs from the desired optical properties. We show that our trained neural network can automatically design common laser mirror coatings types efficiently.
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| Published in: | EPJ Web of conferences Vol. 307; p. 4057 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article Conference Proceeding |
| Language: | English |
| Published: |
Les Ulis
EDP Sciences
01.01.2024
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| ISSN: | 2100-014X, 2101-6275, 2100-014X |
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| Abstract | We use neural networks to address the challenge of deriving dielectric coating designs from the desired optical properties. We show that our trained neural network can automatically design common laser mirror coatings types efficiently. |
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| AbstractList | We use neural networks to address the challenge of deriving dielectric coating designs from the desired optical properties. We show that our trained neural network can automatically design common laser mirror coatings types efficiently. |
| Author | Heyl, Christoph Hartl, Ingmar Carstens, Florian Chattopadhyay, Utsa Wienke, Andreas Ay, Nihat Tünnermann, Henrik |
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| Cites_doi | 10.1002/vipr.200890042 10.1364/AO.35.005493 |
| ContentType | Journal Article Conference Proceeding |
| Copyright | 2024. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and conditions, you may use this content in accordance with the terms of the License. |
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| DOI | 10.1051/epjconf/202430704057 |
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| SubjectTerms | Neural networks Optical coatings Optical properties |
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| Title | Neural Network Method for Dielectric Optical Coating Design |
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