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Computational methods for random differential equations: probability density function and estimation of the parameters

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Computational methods for random differential equations: probability density function and estimation of the parameters

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Calatayud Gregori, J. (2020). Computational methods for random differential equations: probability density function and estimation of the parameters [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/138396

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Title: Computational methods for random differential equations: probability density function and estimation of the parameters
Author: Calatayud Gregori, Julia
Director(s): Cortés López, Juan Carlos
UPV Unit: Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada
Read date / Event date:
2020-01-31
Issued date:
Abstract:
[EN] Mathematical models based on deterministic differential equations do not take into account the inherent uncertainty of the physical phenomenon (in a wide sense) under study. In addition, inaccuracies in the collected ...[+]


[ES] Los modelos matemáticos basados en ecuaciones diferenciales deterministas no tienen en cuenta la incertidumbre inherente del fenómeno físico (en un sentido amplio) bajo estudio. Además, a menudo se producen inexactitudes ...[+]


[CA] Els models matemàtics basats en equacions diferencials deterministes no tenen en compte la incertesa inherent al fenomen físic (en un sentit ampli) sota estudi. A més a més, sovint es produeixen inexactituds en les ...[+]
Subjects: Random ordinary and partial differential equation , Uncertainty quantification , Probability density function , Spectral expansions , Bayesian inverse problem
Copyrigths: Reserva de todos los derechos
DOI: 10.4995/Thesis/10251/138396
Publisher:
Universitat Politècnica de València
Project ID:
MINECO/t MTM2017–89664–P
Thanks:
This work has been supported by the Spanish Ministerio de Econom´ıa y Competitividad grant MTM2017–89664–P.
Type: Tesis doctoral

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