On the generalized logistic random differential equation: Theoretical analysis and numerical simulations with real-world data
![Thumbnail](/xmlui/bitstream/handle/10234/201547/83591.pdf.jpg?sequence=4&isAllowed=y)
Visualitza/
Impacte
![Google Scholar](/xmlui/themes/Mirage2/images/uji/logo_google.png)
![Microsoft Academico](/xmlui/themes/Mirage2/images/uji/logo_microsoft.png)
Metadades
Mostra el registre complet de l'elementcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
comunitat-uji-handle4:
INVESTIGACIONMetadades
Títol
On the generalized logistic random differential equation: Theoretical analysis and numerical simulations with real-world dataData de publicació
2022-08-27Editor
ElsevierCita bibliogràfica
BEVIA, V., et al. On the generalized logistic random differential equation: Theoretical analysis and numerical simulations with real-world data. Communications in Nonlinear Science and Numerical Simulation, 2023, vol. 116, p. 106832.Tipus de document
info:eu-repo/semantics/articleVersió
info:eu-repo/semantics/publishedVersionParaules clau / Matèries
Resum
Based on the previous literature about the random logistic and Gompertz models, the aim of this paper is to extend the investigations to the generalized logistic differential equation in the random setting. First, ... [+]
Based on the previous literature about the random logistic and Gompertz models, the aim of this paper is to extend the investigations to the generalized logistic differential equation in the random setting. First, this is done by rigorously constructing its solution in two different ways, namely, the sample-path approach and the mean-square calculus. Secondly, the probability density function at each time instant is derived in two ways: by applying the random variable transformation technique and by solving the associated Liouville’s partial differential equation. It is also proved that both the stochastic solution and its density function converge, under specific conditions, to the corresponding solution and density function of the logistic and Gompertz models, respectively. The investigation finishes showing some examples, where a number of computational techniques are combined to construct reliable approximations of the probability density of the stochastic solution. In particular, we show, step-by-step, how our findings can be applied to a real-world problem. [-]
Publicat a
Communications in Nonlinear Science and Numerical Simulation, 2023, vol. 116Entitat finançadora
Agencia Estatal de Investigación (AEI), Spain | Universitat Politècnica de València
Codi del projecte o subvenció
PID2020-115270GB-I00
Drets d'accés
© 2022 The Author(s). Published by Elsevier B.V.
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
Apareix a les col.leccions
- MAT_Articles [765]