Accurate and efficient prediction of photonic crystal waveguide bandstructures using neural networks Article Swipe
Caspar F. Schwahn
,
Sebastian A. Schulz
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1364/optcon.485342
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1364/optcon.485342
We demonstrate the use of neural networks to predict the optical properties of photonic crystal waveguides (PhCWs) with high accuracy and significantly faster computation times compared to traditional simulation methods. Using 100,000 PhCW designs and their simulated bandstructures, we trained a neural network to achieve a test set relative error of 0.103% in predicting gap guided bands. We use pre-training to improve neural network performance, and numerical differentiation to accurately predict group index curves. Our approach allows for rapid, application-specific tailoring of PhCWs with a runtime of sub-milliseconds per design, a significant improvement over conventional simulation techniques.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1364/optcon.485342
- OA Status
- gold
- Cited By
- 3
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4372291683
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https://openalex.org/W4372291683Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1364/optcon.485342Digital Object Identifier
- Title
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Accurate and efficient prediction of photonic crystal waveguide bandstructures using neural networksWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-05-05Full publication date if available
- Authors
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Caspar F. Schwahn, Sebastian A. SchulzList of authors in order
- Landing page
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https://doi.org/10.1364/optcon.485342Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1364/optcon.485342Direct OA link when available
- Concepts
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Artificial neural network, Computation, Computer science, Set (abstract data type), Photonic crystal, Test set, Algorithm, Artificial intelligence, Electronic engineering, Optics, Engineering, Physics, Programming languageTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2024: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.improvement | 92 |
| abstract_inverted_index.significant | 91 |
| abstract_inverted_index.techniques. | 96 |
| abstract_inverted_index.traditional | 27 |
| abstract_inverted_index.conventional | 94 |
| abstract_inverted_index.performance, | 64 |
| abstract_inverted_index.pre-training | 59 |
| abstract_inverted_index.significantly | 21 |
| abstract_inverted_index.bandstructures, | 37 |
| abstract_inverted_index.differentiation | 67 |
| abstract_inverted_index.sub-milliseconds | 87 |
| abstract_inverted_index.application-specific | 79 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 96 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.7012421 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |