Optimal control by deep learning techniques and its applications on epidemic models

We represent the optimal control functions by neural networks and solve optimal control problems by deep learning techniques. Adjoint sensitivity analysis is applied to train the neural networks embedded in differential equations. This method can not only be applied in classic epidemic control probl...

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Detalles Bibliográficos
Autores principales: Yin, Shuangshuang, Wu, Jianhong, Song, Pengfei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9875778/
https://www.ncbi.nlm.nih.gov/pubmed/36695914
http://dx.doi.org/10.1007/s00285-023-01873-0
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author Yin, Shuangshuang
Wu, Jianhong
Song, Pengfei
author_facet Yin, Shuangshuang
Wu, Jianhong
Song, Pengfei
author_sort Yin, Shuangshuang
collection PubMed
description We represent the optimal control functions by neural networks and solve optimal control problems by deep learning techniques. Adjoint sensitivity analysis is applied to train the neural networks embedded in differential equations. This method can not only be applied in classic epidemic control problems, but also in epidemic forecasting, discovering unknown mechanisms, and the ideas behind can give new insights to traditional mathematical epidemiological problems.
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spelling pubmed-98757782023-01-25 Optimal control by deep learning techniques and its applications on epidemic models Yin, Shuangshuang Wu, Jianhong Song, Pengfei J Math Biol Article We represent the optimal control functions by neural networks and solve optimal control problems by deep learning techniques. Adjoint sensitivity analysis is applied to train the neural networks embedded in differential equations. This method can not only be applied in classic epidemic control problems, but also in epidemic forecasting, discovering unknown mechanisms, and the ideas behind can give new insights to traditional mathematical epidemiological problems. Springer Berlin Heidelberg 2023-01-25 2023 /pmc/articles/PMC9875778/ /pubmed/36695914 http://dx.doi.org/10.1007/s00285-023-01873-0 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Yin, Shuangshuang
Wu, Jianhong
Song, Pengfei
Optimal control by deep learning techniques and its applications on epidemic models
title Optimal control by deep learning techniques and its applications on epidemic models
title_full Optimal control by deep learning techniques and its applications on epidemic models
title_fullStr Optimal control by deep learning techniques and its applications on epidemic models
title_full_unstemmed Optimal control by deep learning techniques and its applications on epidemic models
title_short Optimal control by deep learning techniques and its applications on epidemic models
title_sort optimal control by deep learning techniques and its applications on epidemic models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9875778/
https://www.ncbi.nlm.nih.gov/pubmed/36695914
http://dx.doi.org/10.1007/s00285-023-01873-0
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AT songpengfei optimalcontrolbydeeplearningtechniquesanditsapplicationsonepidemicmodels

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