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JiaweiSunTheses.pdf (3.39 MB)
ETD Abstract Container
Abstract Header
Discontinuous Galerkin method for Boussinesq system and stochastic Maxwell equations
Author Info
Sun, Jiawei
ORCID® Identifier
http://orcid.org/0000-0003-0542-6871
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=osu165028885151042
Abstract Details
Year and Degree
2022, Doctor of Philosophy, Ohio State University, Mathematics.
Abstract
Partial differential equations (PDEs) are commonly used for characterizing physical phenomena such as shallow water equations, water wave problems, electromagnetism, etc. This dissertation contains two parts dedicated to the advancement of numerical methods for two types of PDEs: the abcd-Boussinesq system and the stochastic Maxwell equations. We use discontinuous Galerkin (DG) method as our tool for numerically solving these equations. For the first part, we considered the abcd Boussinesq system which is a family of Boussinesq type equations including many well-known models such as the classical Boussinesq system, BBM-BBM system, Bona-Smith system etc. We proposed local discontinuous Galerkin (LDG) methods, with carefully chosen numerical fluxes, to numerically solve this abcd Boussinesq system. The main focus of this project is to rigorously establish a priori error estimate of the proposed LDG methods for a wide range of the parameters a,b,c,d. Numerical examples are also provided to demonstrate the convergence rates and to show that the proposed method has the capability to simulate the head-on collision and finite time blow-up behavior well. In the second part, firstly we considered one and multi-dimensional stochastic Maxwell equations with additive noise. Such system can also be written in the multi-symplectic structure, and the stochastic energy increases linearly in time. We designed high order DG methods for the stochastic Maxwell equations with additive noise, and we showed that the proposed methods satisfy the discrete form of the stochastic energy linear growth property and preserve the multi-symplectic structure in the discrete level. Optimal error estimate of the semi-discrete DG method is also obtained. The fully discrete methods are derived from coupling with symplectic temporal discretization. One- and two-dimensional numerical results are provided to demonstrate the performance of optimal error estimates and linear growth of the discrete energy. Secondly, we studied stochastic Maxwell equation with general multiplicative noise, which is a generalization of the additive noise. High order DG method is also applied to the one and two dimensional cases. Discrete version of stability of numerical solutions is derived. We analyzed the convergence order of our numerical scheme and then obtained optimal error estimate. The full discretization combines DG scheme in space and strong Taylor scheme in time. Numerical implementations are provided to demonstrate the optimal convergence rate in both one and two dimensional cases.
Committee
Yulong Xing (Advisor)
Dongbin Xiu (Committee Member)
Barbara Keyfitz (Committee Member)
Subject Headings
Applied Mathematics
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Citations
Sun, J. (2022).
Discontinuous Galerkin method for Boussinesq system and stochastic Maxwell equations
[Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu165028885151042
APA Style (7th edition)
Sun, Jiawei.
Discontinuous Galerkin method for Boussinesq system and stochastic Maxwell equations .
2022. Ohio State University, Doctoral dissertation.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=osu165028885151042.
MLA Style (8th edition)
Sun, Jiawei. "Discontinuous Galerkin method for Boussinesq system and stochastic Maxwell equations ." Doctoral dissertation, Ohio State University, 2022. http://rave.ohiolink.edu/etdc/view?acc_num=osu165028885151042
Chicago Manual of Style (17th edition)
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Document number:
osu165028885151042
Download Count:
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Copyright Info
© 2022, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.