Optimal design of topological waveguides by machine learning Article Swipe
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· 2022
· Open Access
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· DOI: https://doi.org/10.3389/fmats.2022.1075073
Topological insulators supply robust edge states and can be used to compose novel waveguides to protect energy propagation against various defects. For practical applications, topological waveguides with a large working bandwidth and highly localized interface mode are desired. In the present work, mechanical valley Hall insulators are described by explicit geometry parameters using the moving morphable component method first. From the geometry parameters, artificial neural networks (ANN) are then well-trained to predict the topological property and the bounds of nontrivial bandgaps. Incorporating those ANN models, mathematical formulation for designing optimal mechanical topological waveguides can be solved efficiently, with an acceleration of more than 10,000 times than the traditional topology optimization approach.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fmats.2022.1075073
- https://www.frontiersin.org/articles/10.3389/fmats.2022.1075073/pdf
- OA Status
- gold
- Cited By
- 13
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4311719971
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4311719971Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fmats.2022.1075073Digital Object Identifier
- Title
-
Optimal design of topological waveguides by machine learningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-12-05Full publication date if available
- Authors
-
Zongliang Du, Xianggui Ding, Hui Chen, Chang Liu, Weisheng Zhang, Jiachen Luo, Xu GuoList of authors in order
- Landing page
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https://doi.org/10.3389/fmats.2022.1075073Publisher landing page
- PDF URL
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https://www.frontiersin.org/articles/10.3389/fmats.2022.1075073/pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.frontiersin.org/articles/10.3389/fmats.2022.1075073/pdfDirect OA link when available
- Concepts
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Topology (electrical circuits), Topological insulator, Artificial neural network, Topology optimization, Bandwidth (computing), Enhanced Data Rates for GSM Evolution, Waveguide, Computer science, Physics, Mathematics, Optics, Finite element method, Artificial intelligence, Telecommunications, Quantum mechanics, Thermodynamics, CombinatoricsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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13Total citation count in OpenAlex
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2025: 3, 2024: 2, 2023: 8Per-year citation counts (last 5 years)
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44Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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