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dc.contributor.advisorYan, Yubinen
dc.contributor.authorPatel, Babubhai M.*
dc.date.accessioned2011-02-11T10:28:31Zen
dc.date.available2011-02-11T10:28:31Zen
dc.date.issued2009-09en
dc.identifier.urihttp://hdl.handle.net/10034/121676en
dc.description.abstractDifferential equations, especially partial differential equations (PDES) have wide range of applications in sciences, finance (economics), Engineering and so forth. In last decade, substantial amount of work has been done in studying stochastic partial differential equations (SPDES). A SPDE is a PDE containing a random ‘noise’ term. SPDES have no analytical solutions. Various numerical methods have been developed from time to time and tested for their validity using Matlab program. In this thesis, the author will discuss the finite difference method for stochastic parabolic partial differential equations. Matlab software is used for simulation of the solution of this equation. The main objective of this thesis is to investigate the finite difference approximation of a stochastic parabolic partial differential equation with white noise. The author discusses alternative proof for error bounds using Green function in support of this method.
dc.language.isoenen
dc.publisherUniversity of Chesteren
dc.subjectstochastic parabolic partial differential equationen
dc.subjectwhite noiseen
dc.subjectgreen functionen
dc.subjectBrownian motionen
dc.subjectisometry propertyen
dc.subjectforward Euler methoden
dc.subjectbackward Euler methoden
dc.subjectCrank-Nicolson methoden
dc.titleFinite difference approximation for stochastic parabolic partial differential equationsen
dc.typeThesis or dissertationen
dc.type.qualificationnameMScen
dc.type.qualificationlevelMasters Degreeen
html.description.abstractDifferential equations, especially partial differential equations (PDES) have wide range of applications in sciences, finance (economics), Engineering and so forth. In last decade, substantial amount of work has been done in studying stochastic partial differential equations (SPDES). A SPDE is a PDE containing a random ‘noise’ term. SPDES have no analytical solutions. Various numerical methods have been developed from time to time and tested for their validity using Matlab program. In this thesis, the author will discuss the finite difference method for stochastic parabolic partial differential equations. Matlab software is used for simulation of the solution of this equation. The main objective of this thesis is to investigate the finite difference approximation of a stochastic parabolic partial differential equation with white noise. The author discusses alternative proof for error bounds using Green function in support of this method.


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