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dc.contributor.author | Bevia-Escrig, Vicente-José![]() |
es_ES |
dc.contributor.author | Burgos-Simon, Clara![]() |
es_ES |
dc.contributor.author | Cortés, J.-C.![]() |
es_ES |
dc.contributor.author | Villanueva Micó, Rafael Jacinto![]() |
es_ES |
dc.date.accessioned | 2022-01-30T19:06:51Z | |
dc.date.available | 2022-01-30T19:06:51Z | |
dc.date.issued | 2021-06 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/180371 | |
dc.description.abstract | [EN] The Baranyi-Roberts model describes the dynamics of the volumetric densities of two interacting cell populations. We randomize this model by considering that the initial conditions are random variables whose distributions are determined by using sample data and the principle of maximum entropy. Subsequenly, we obtain the Liouville-Gibbs partial differential equation for the probability density function of the two-dimensional solution stochastic process. Because the exact solution of this equation is unaffordable, we use a finite volume scheme to numerically approximate the aforementioned probability density function. From this key information, we design an optimization procedure in order to determine the best growth rates of the Baranyi-Roberts model, so that the expectation of the numerical solution is as close as possible to the sample data. The results evidence good fitting that allows for performing reliable predictions. | es_ES |
dc.description.sponsorship | This work has been supported by the Spanish Ministerio de Economia, Industria y Competitividad (MINECO), the Agencia Estatal de Investigacion (AEI) and Fondo Europeo de Desarrollo Regional (FEDER UE) grant MTM2017-89664-P. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation.ispartof | Fractal and Fractional | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Uncertainty quantification | es_ES |
dc.subject | Competitive stochastic model | es_ES |
dc.subject | Model simulation | es_ES |
dc.subject | Model prediction | es_ES |
dc.subject | Principle of maximum entropy | es_ES |
dc.subject | Optimization | es_ES |
dc.subject.classification | MATEMATICA APLICADA | es_ES |
dc.title | Uncertainty Quantification of Random Microbial Growth in a Competitive Environment via Probability Density Functions | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.3390/fractalfract5020026 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2017-89664-P/ES/PROBLEMAS DINAMICOS CON INCERTIDUMBRE SIMULABLE: MODELIZACION MATEMATICA, ANALISIS, COMPUTACION Y APLICACIONES/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Instituto Universitario de Matemática Multidisciplinar - Institut Universitari de Matemàtica Multidisciplinària | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada | es_ES |
dc.description.bibliographicCitation | Bevia-Escrig, V.; Burgos-Simon, C.; Cortés, J.; Villanueva Micó, RJ. (2021). Uncertainty Quantification of Random Microbial Growth in a Competitive Environment via Probability Density Functions. Fractal and Fractional. 5(2):1-18. https://doi.org/10.3390/fractalfract5020026 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.3390/fractalfract5020026 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 18 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 5 | es_ES |
dc.description.issue | 2 | es_ES |
dc.identifier.eissn | 2504-3110 | es_ES |
dc.relation.pasarela | S\442937 | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |
dc.contributor.funder | European Regional Development Fund | es_ES |