Leveraging long short-term memory (LSTM)-based neural networks for modeling structure–property relationships of metamaterials from electromagnetic responses

We report a neural network model for predicting the electromagnetic response of mesoscale metamaterials as well as generate design parameters for a desired spectral behavior. Our approach entails treating spectral data as time-varying sequences and the inverse problem as a single-input multiple outp...

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Autores principales: Pillai, Prajith, Pal, Parama, Chacko, Rinu, Jain, Deepak, Rai, Beena
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8452651/
https://www.ncbi.nlm.nih.gov/pubmed/34545123
http://dx.doi.org/10.1038/s41598-021-97999-6
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author Pillai, Prajith
Pal, Parama
Chacko, Rinu
Jain, Deepak
Rai, Beena
author_facet Pillai, Prajith
Pal, Parama
Chacko, Rinu
Jain, Deepak
Rai, Beena
author_sort Pillai, Prajith
collection PubMed
description We report a neural network model for predicting the electromagnetic response of mesoscale metamaterials as well as generate design parameters for a desired spectral behavior. Our approach entails treating spectral data as time-varying sequences and the inverse problem as a single-input multiple output model, thereby compelling the network architecture to learn the geometry of the metamaterial designs from the spectral data in lieu of abstract features.
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spelling pubmed-84526512021-09-21 Leveraging long short-term memory (LSTM)-based neural networks for modeling structure–property relationships of metamaterials from electromagnetic responses Pillai, Prajith Pal, Parama Chacko, Rinu Jain, Deepak Rai, Beena Sci Rep Article We report a neural network model for predicting the electromagnetic response of mesoscale metamaterials as well as generate design parameters for a desired spectral behavior. Our approach entails treating spectral data as time-varying sequences and the inverse problem as a single-input multiple output model, thereby compelling the network architecture to learn the geometry of the metamaterial designs from the spectral data in lieu of abstract features. Nature Publishing Group UK 2021-09-20 /pmc/articles/PMC8452651/ /pubmed/34545123 http://dx.doi.org/10.1038/s41598-021-97999-6 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Pillai, Prajith
Pal, Parama
Chacko, Rinu
Jain, Deepak
Rai, Beena
Leveraging long short-term memory (LSTM)-based neural networks for modeling structure–property relationships of metamaterials from electromagnetic responses
title Leveraging long short-term memory (LSTM)-based neural networks for modeling structure–property relationships of metamaterials from electromagnetic responses
title_full Leveraging long short-term memory (LSTM)-based neural networks for modeling structure–property relationships of metamaterials from electromagnetic responses
title_fullStr Leveraging long short-term memory (LSTM)-based neural networks for modeling structure–property relationships of metamaterials from electromagnetic responses
title_full_unstemmed Leveraging long short-term memory (LSTM)-based neural networks for modeling structure–property relationships of metamaterials from electromagnetic responses
title_short Leveraging long short-term memory (LSTM)-based neural networks for modeling structure–property relationships of metamaterials from electromagnetic responses
title_sort leveraging long short-term memory (lstm)-based neural networks for modeling structure–property relationships of metamaterials from electromagnetic responses
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8452651/
https://www.ncbi.nlm.nih.gov/pubmed/34545123
http://dx.doi.org/10.1038/s41598-021-97999-6
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