A neural operator-based surrogate solver for free-form electromagnetic inverse design Article Swipe
Yannick Augenstein
,
Taavi Repän
,
Carsten Rockstuhl
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2302.01934
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2302.01934
Neural operators have emerged as a powerful tool for solving partial differential equations in the context of scientific machine learning. Here, we implement and train a modified Fourier neural operator as a surrogate solver for electromagnetic scattering problems and compare its data efficiency to existing methods. We further demonstrate its application to the gradient-based nanophotonic inverse design of free-form, fully three-dimensional electromagnetic scatterers, an area that has so far eluded the application of deep learning techniques.
Related Topics
Concepts
Solver
Computer science
Operator (biology)
Electromagnetics
Context (archaeology)
Computational electromagnetics
Inverse problem
Artificial neural network
Convolutional neural network
Inverse
Surrogate model
Computational science
Mathematical optimization
Artificial intelligence
Electromagnetic field
Machine learning
Electronic engineering
Mathematics
Physics
Mathematical analysis
Engineering
Transcription factor
Paleontology
Chemistry
Geometry
Quantum mechanics
Repressor
Biology
Gene
Programming language
Biochemistry
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2302.01934
- https://arxiv.org/pdf/2302.01934
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4319451594
All OpenAlex metadata
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https://openalex.org/W4319451594Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2302.01934Digital Object Identifier
- Title
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A neural operator-based surrogate solver for free-form electromagnetic inverse designWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-02-04Full publication date if available
- Authors
-
Yannick Augenstein, Taavi Repän, Carsten RockstuhlList of authors in order
- Landing page
-
https://arxiv.org/abs/2302.01934Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2302.01934Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2302.01934Direct OA link when available
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Solver, Computer science, Operator (biology), Electromagnetics, Context (archaeology), Computational electromagnetics, Inverse problem, Artificial neural network, Convolutional neural network, Inverse, Surrogate model, Computational science, Mathematical optimization, Artificial intelligence, Electromagnetic field, Machine learning, Electronic engineering, Mathematics, Physics, Mathematical analysis, Engineering, Transcription factor, Paleontology, Chemistry, Geometry, Quantum mechanics, Repressor, Biology, Gene, Programming language, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| primary_location.raw_type | text |
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| publication_year | 2023 |
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