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

Abstract 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 mult...

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Autores principales:	Prajith Pillai, Parama Pal, Rinu Chacko, Deepak Jain, Beena Rai
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/796cc93285054dd3ab74a27411802243
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spelling	oai:doaj.org-article:796cc93285054dd3ab74a274118022432021-12-02T18:14:00ZLeveraging long short-term memory (LSTM)-based neural networks for modeling structure–property relationships of metamaterials from electromagnetic responses10.1038/s41598-021-97999-62045-2322https://doaj.org/article/796cc93285054dd3ab74a274118022432021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97999-6https://doaj.org/toc/2045-2322Abstract 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.Prajith PillaiParama PalRinu ChackoDeepak JainBeena RaiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-6 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Prajith Pillai Parama Pal Rinu Chacko Deepak Jain Beena Rai Leveraging long short-term memory (LSTM)-based neural networks for modeling structure–property relationships of metamaterials from electromagnetic responses
description	Abstract 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.
format	article
author	Prajith Pillai Parama Pal Rinu Chacko Deepak Jain Beena Rai
author_facet	Prajith Pillai Parama Pal Rinu Chacko Deepak Jain Beena Rai
author_sort	Prajith Pillai
title	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_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_sort	leveraging long short-term memory (lstm)-based neural networks for modeling structure–property relationships of metamaterials from electromagnetic responses
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/796cc93285054dd3ab74a27411802243
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Leveraging long short-term memory (LSTM)-based neural networks for modeling structure–property relationships of metamaterials from electromagnetic responses

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