Location of sources in reaction-diffusion equations using support vector machines.

The reaction-diffusion equation serves to model systems in the diffusion regime with sources. Specific applications include diffusion processes in chemical reactions, as well as the propagation of species, diseases, and populations in general. In some of these applications the location of an outbrea...

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Main Authors: Venecia Chávez-Medina, José A González, Francisco S Guzmán
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2019-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0225593
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spelling doaj-133546efdeee495794ed9172e836d0612021-03-03T21:20:32ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-011412e022559310.1371/journal.pone.0225593Location of sources in reaction-diffusion equations using support vector machines.Venecia Chávez-MedinaJosé A GonzálezFrancisco S GuzmánThe reaction-diffusion equation serves to model systems in the diffusion regime with sources. Specific applications include diffusion processes in chemical reactions, as well as the propagation of species, diseases, and populations in general. In some of these applications the location of an outbreak, for instance, the source point of a disease or the nest of a vector spreading a virus is important. Also important are the environmental parameters of the domain where the process diffuses, namely the space-dependent diffusion coefficient and the proliferation parameter of the process. Determining both, the location of a source and the environmental parameters, define an inverse problem that in turn, involves a partial differential equation. In this paper we classify the values of these parameters using Support Vector Machines (SVM) trained with numerical solutions of the reaction-diffusion problem. Our set up has accuracy of classifying the outbreak location above 90% and 77% of classifying both, the location and the environmental parameters. The approach presented in our analysis can be directly implemented by measuring the population under study at specific locations in the spatial domain as function of time.https://doi.org/10.1371/journal.pone.0225593
collection DOAJ
language English
format Article
sources DOAJ
author Venecia Chávez-Medina
José A González
Francisco S Guzmán
spellingShingle Venecia Chávez-Medina
José A González
Francisco S Guzmán
Location of sources in reaction-diffusion equations using support vector machines.
PLoS ONE
author_facet Venecia Chávez-Medina
José A González
Francisco S Guzmán
author_sort Venecia Chávez-Medina
title Location of sources in reaction-diffusion equations using support vector machines.
title_short Location of sources in reaction-diffusion equations using support vector machines.
title_full Location of sources in reaction-diffusion equations using support vector machines.
title_fullStr Location of sources in reaction-diffusion equations using support vector machines.
title_full_unstemmed Location of sources in reaction-diffusion equations using support vector machines.
title_sort location of sources in reaction-diffusion equations using support vector machines.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2019-01-01
description The reaction-diffusion equation serves to model systems in the diffusion regime with sources. Specific applications include diffusion processes in chemical reactions, as well as the propagation of species, diseases, and populations in general. In some of these applications the location of an outbreak, for instance, the source point of a disease or the nest of a vector spreading a virus is important. Also important are the environmental parameters of the domain where the process diffuses, namely the space-dependent diffusion coefficient and the proliferation parameter of the process. Determining both, the location of a source and the environmental parameters, define an inverse problem that in turn, involves a partial differential equation. In this paper we classify the values of these parameters using Support Vector Machines (SVM) trained with numerical solutions of the reaction-diffusion problem. Our set up has accuracy of classifying the outbreak location above 90% and 77% of classifying both, the location and the environmental parameters. The approach presented in our analysis can be directly implemented by measuring the population under study at specific locations in the spatial domain as function of time.
url https://doi.org/10.1371/journal.pone.0225593
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AT joseagonzalez locationofsourcesinreactiondiffusionequationsusingsupportvectormachines
AT franciscosguzman locationofsourcesinreactiondiffusionequationsusingsupportvectormachines
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