Non-Gaussian Quadrature Integral Transform Solution of Parabolic Models with a Finite Degree of Randomness

María-Consuelo Casabán, Rafael Company and Lucas Jódar
Additional contact information
María-Consuelo Casabán: Instituto Universitario de Matemática Multidisciplinar, Building 8G, access C, 2nd floor, 46022 Valencia, Spain
Rafael Company: Instituto Universitario de Matemática Multidisciplinar, Building 8G, access C, 2nd floor, 46022 Valencia, Spain
Lucas Jódar: Instituto Universitario de Matemática Multidisciplinar, Building 8G, access C, 2nd floor, 46022 Valencia, Spain

Mathematics, 2020, vol. 8, issue 7, 1-16

Abstract: In this paper, we propose an integral transform method for the numerical solution of random mean square parabolic models, that makes manageable the computational complexity due to the storage of intermediate information when one applies iterative methods. By applying the random Laplace transform method combined with the use of Monte Carlo and numerical integration of the Laplace transform inversion, an easy expression of the approximating stochastic process allows the manageable computation of the statistical moments of the approximation.

Keywords: random mean square parabolic model; Laplace transform; numerical inverse Laplace integration; numerical simulation; Monte Carlo method (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/8/7/1112/pdf (application/pdf)
https://www.mdpi.com/2227-7390/8/7/1112/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:8:y:2020:i:7:p:1112-:d:380895

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().