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dc.contributor.advisorYan, Yubin
dc.contributor.authorKhan, Monzorul
dc.date.accessioned2021-03-18T09:13:18Z
dc.date.available2021-03-18T09:13:18Z
dc.date.issued2020-03
dc.identifierhttps://chesterrep.openrepository.com/bitstream/handle/10034/624359/PhD%20Dissertation%20%28Completed%29%20%281%29.pdf?sequence=1
dc.identifier.citationKhan, M. (2020). Numerical methods for deterministic and stochastic fractional partial differential equations (Doctoral dissertation). University of Chester, United Kingdom.en_US
dc.identifier.urihttp://hdl.handle.net/10034/624359
dc.description.abstractIn this thesis we will explore the numerical methods for solving deterministic and stochastic space and time fractional partial differential equations. Firstly we consider Fourier spectral methods for solving some linear stochastic space fractional partial differential equations perturbed by space-time white noises in one dimensional case. The space fractional derivative is defined by using the eigenvalues and eigenfunctions of Laplacian subject to some boundary conditions. We approximate the space-time white noise by using piecewise constant functions and obtain the approximated stochastic space fractional partial differential equations. The approximated stochastic space fractional partial differential equations are then solved by using Fourier spectral methods. Secondly we consider Fourier spectral methods for solving stochastic space fractional partial differential equation driven by special additive noises in one dimensional case. The space fractional derivative is defined by using the eigenvalues and eigenfunctions of Laplacian subject to some boundary conditions. The space-time noise is approximated by the piecewise constant functions in the time direction and by appropriate approximations in the space direction. The approximated stochastic space fractional partial differential equation is then solved by using Fourier spectral methods. Thirdly, we will consider the discontinuous Galerkin time stepping methods for solving the linear space fractional partial differential equations. The space fractional derivatives are defined by using Riesz fractional derivative. The space variable is discretized by means of a Galerkin finite element method and the time variable is discretized by the discontinous Galerkin method. The approximate solution will be sought as a piecewise polynomial function in t of degree at most q−1, q ≥ 1, which is not necessarily continuous at the nodes of the defining partition. The error estimates in the fully discrete case are obtained and the numerical examples are given. Finally, we consider error estimates for the modified L1 scheme for solving time fractional partial differential equation. Jin et al. (2016, An analysis of the L1 scheme for the subdiffifusion equation with nonsmooth data, IMA J. of Number. Anal., 36, 197-221) ii established the O(k) convergence rate for the L1 scheme for both smooth and nonsmooth initial data. We introduce a modified L1 scheme and prove that the convergence rate is O(k2−α=), 0 < α < 1 for both smooth and nonsmooth initial data. We first write the time fractional partial differential equations as a Volterra integral equation which is then approximated by using the convolution quadrature with some special generating functions. A Laplace transform method is used to prove the error estimates for the homogeneous time fractional partial differential equation for both smooth and nonsmooth data. Numerical examples are given to show that the numerical results are consistent with the theoretical results.en_US
dc.language.isoenen_US
dc.publisherUniversity of Chesteren_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectnumerical methodsen_US
dc.subjectdifferential equationsen_US
dc.subjectFourier spectral methodsen_US
dc.titleNumerical methods for deterministic and stochastic fractional partial differential equationsen_US
dc.typeThesis or dissertationen_US
dc.type.qualificationnamePhDen_US
dc.type.qualificationlevelDoctoralen_US


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