Transfer learning for metamaterial design and simulation Article Swipe
Rixi Peng
,
Simiao Ren
,
Jordan M. Malof
,
Willie J. Padilla
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1515/nanoph-2023-0691
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1515/nanoph-2023-0691
We demonstrate transfer learning as a tool to improve the efficacy of training deep learning models based on residual neural networks (ResNets). Specifically, we examine its use for study of multi-scale electrically large metasurface arrays under open boundary conditions in electromagnetic metamaterials. Our aim is to assess the efficiency of transfer learning across a range of problem domains that vary in their resemblance to the original base problem for which the ResNet model was initially trained. We use a quasi-analytical discrete dipole approximation (DDA) method to simulate electrically large metasurface arrays to obtain ground truth data for training and testing of our deep neural network. Our approach can save significant time for examining novel metasurface designs by harnessing the power of transfer learning, as it effectively mitigates the pervasive data bottleneck issue commonly encountered in deep learning. We demonstrate that for the best case when the transfer task is sufficiently similar to the target task, a new task can be effectively trained using only a few data points yet still achieve a test mean absolute relative error of 3 % with a pre-trained neural network, realizing data reduction by a factor of 1000.
Related Topics
Concepts
Computer science
Bottleneck
Transfer of learning
Metamaterial
Artificial neural network
Deep learning
Artificial intelligence
Task (project management)
Inductive transfer
Reduction (mathematics)
Machine learning
Residual
Algorithm
Physics
Optics
Mobile robot
Robot
Mathematics
Geometry
Economics
Embedded system
Robot learning
Management
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1515/nanoph-2023-0691
- https://www.degruyter.com/document/doi/10.1515/nanoph-2023-0691/pdf
- OA Status
- gold
- Cited By
- 13
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4393090928
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4393090928Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1515/nanoph-2023-0691Digital Object Identifier
- Title
-
Transfer learning for metamaterial design and simulationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-03-22Full publication date if available
- Authors
-
Rixi Peng, Simiao Ren, Jordan M. Malof, Willie J. PadillaList of authors in order
- Landing page
-
https://doi.org/10.1515/nanoph-2023-0691Publisher landing page
- PDF URL
-
https://www.degruyter.com/document/doi/10.1515/nanoph-2023-0691/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
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https://www.degruyter.com/document/doi/10.1515/nanoph-2023-0691/pdfDirect OA link when available
- Concepts
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Computer science, Bottleneck, Transfer of learning, Metamaterial, Artificial neural network, Deep learning, Artificial intelligence, Task (project management), Inductive transfer, Reduction (mathematics), Machine learning, Residual, Algorithm, Physics, Optics, Mobile robot, Robot, Mathematics, Geometry, Economics, Embedded system, Robot learning, ManagementTop concepts (fields/topics) attached by OpenAlex
- Cited by
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13Total citation count in OpenAlex
- Citations by year (recent)
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2025: 10, 2024: 3Per-year citation counts (last 5 years)
- References (count)
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36Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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