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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 7/2021

25.06.2020 | Original Article

Prediction of electromagnetic field patterns of optical waveguide using neural network

verfasst von: Gandhi Alagappan, Ching Eng Png

Erschienen in: Neural Computing and Applications | Ausgabe 7/2021

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Abstract

Physical fields represent quantities that vary in space and/or time axes. Understanding the distribution of a field pattern is a key element in scientific discoveries and technological developments. In this article, by picking up the electromagnetic field of an optical waveguide as an example, we demonstrate how field patterns can be uncovered using artificial neural networks. The cross section plane of the optical waveguide is discretized into a set of tiny pixels, and the field values are obtained at these pixels. Deep learning model is created by assuming the field values as outputs, and the geometrical dimensions of the waveguide as inputs. The correlation between the field values in the adjacent pixels is established by mean of feedback using a recurrent neural network. The trained deep learning model enables field pattern prediction for the entire (and usual) parameter space for applications in the field of photonics.
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Metadaten
Titel
Prediction of electromagnetic field patterns of optical waveguide using neural network
verfasst von
Gandhi Alagappan
Ching Eng Png
Publikationsdatum
25.06.2020
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 7/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05061-9

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