Exploiting graph neural networks to perform finite-difference time-domain based optical simulations Article Swipe
Lukas Kuhn
,
Taavi Repän
,
Carsten Rockstuhl
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1063/5.0139004
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1063/5.0139004
Having an artificial neural network that solves Maxwell’s equations in a general setting is an intellectual challenge and a great utility. Recently, there have been multiple successful attempts to use artificial neural networks to predict electromagnetic fields, given a specific source and interacting material distribution. However, many of these attempts are limited in domain size and restricted to object shapes similar to the learned ones. Here, we overcome these restrictions by using graph neural networks (GNNs) that adapt the propagation scheme of the finite-difference time-domain (FDTD) method to solve Maxwell’s equations for a distinct time step. GNNs yield a significant advantage, i.e., size invariance, over conventional neural network architectures, such as convolutional or linear neural networks. Once trained, a GNN can work on graphs of arbitrary size and connectivity. This allows us to train them on the propagation procedure of electromagnetic fields on small domain sizes and, finally, expand the domain to an arbitrary scale. Moreover, GNNs can adapt to any material shape and work not only on structured grids, such as FDTD, but also on arbitrary meshes. This work may be seen as the first benchmark for field predictions with graph networks and could be expanded to more complex mesh-based optical simulations, e.g., those based on finite elements.
Related Topics
Concepts
Finite-difference time-domain method
Polygon mesh
Computer science
Artificial neural network
Electromagnetic field
Convolutional neural network
Domain (mathematical analysis)
Theoretical computer science
Maxwell's equations
Benchmark (surveying)
Algorithm
Electromagnetism
Artificial intelligence
Mathematics
Mathematical analysis
Physics
Optics
Computer graphics (images)
Quantum mechanics
Geography
Geodesy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1063/5.0139004
- https://aip.scitation.org/doi/pdf/10.1063/5.0139004
- OA Status
- gold
- Cited By
- 14
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4320718470
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4320718470Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1063/5.0139004Digital Object Identifier
- Title
-
Exploiting graph neural networks to perform finite-difference time-domain based optical simulationsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-02-14Full publication date if available
- Authors
-
Lukas Kuhn, Taavi Repän, Carsten RockstuhlList of authors in order
- Landing page
-
https://doi.org/10.1063/5.0139004Publisher landing page
- PDF URL
-
https://aip.scitation.org/doi/pdf/10.1063/5.0139004Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://aip.scitation.org/doi/pdf/10.1063/5.0139004Direct OA link when available
- Concepts
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Finite-difference time-domain method, Polygon mesh, Computer science, Artificial neural network, Electromagnetic field, Convolutional neural network, Domain (mathematical analysis), Theoretical computer science, Maxwell's equations, Benchmark (surveying), Algorithm, Electromagnetism, Artificial intelligence, Mathematics, Mathematical analysis, Physics, Optics, Computer graphics (images), Quantum mechanics, Geography, GeodesyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
14Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 6, 2023: 4Per-year citation counts (last 5 years)
- References (count)
-
39Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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