Image Reconstruction on Electromagnetic Simulation by Using a Recurrent Neural Network

This study demonstrates that images can be inferred using a recurrent neural network trained on a large number of electromagnetic field images as time-series data and that the simulation results can be repeatedly and efficiently reused. © 2024 Hiroshi Fukuda

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Publication Details

Published
2024-08-04
DOI
https://doi.org/10.1109/cleo-pr60912.2024.10676744
Primary Topic
Neural Networks and Applications
Type
article
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article

Image Reconstruction on Electromagnetic Simulation by Using a Recurrent Neural Network

Hiroshi Fukuda
Neural Networks and Applications
article

Image Reconstruction on Electromagnetic Simulation by Using a Recurrent Neural Network

Hiroshi Fukuda
article en

Abstract

This study demonstrates that images can be inferred using a recurrent neural network trained on a large number of electromagnetic field images as time-series data and that the simulation results can be repeatedly and efficiently reused. © 2024 Hiroshi Fukuda

Chitose Institute of Science and Technology (JP)
Openalex Percentile: Top 86%
Neural Networks and Applications
DOISourceOpenAlex
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Image Reconstruction on Electromagnetic Simulation by Using a Recurrent Neural Network — Hiroshi Fukuda · (2024) | TGRS Research Map | TGRS