Search for dissertations about: "Stochastic Partial Differential Equation"
Showing result 1 - 5 of 32 swedish dissertations containing the words Stochastic Partial Differential Equation.
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1. Numerical analysis and simulation of stochastic partial differential equations with white noise dispersion
Abstract : This doctoral thesis provides a comprehensive numerical analysis and exploration of several stochastic partial differential equations (SPDEs). More specifically, this thesis investigates time integrators for SPDEs with white noise dispersion. READ MORE
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2. Approximating Stochastic Partial Differential Equations with Finite Elements: Computation and Analysis
Abstract : Stochastic partial differential equations (SPDE) must be approximated in space and time to allow for the simulation of their solutions. In this thesis fully discrete approximations of such equations are considered, with an emphasis on finite element methods combined with rational semigroup approximations. READ MORE
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3. On weak and strong convergence of numerical approximations of stochastic partial differential equations
Abstract : This thesis is concerned with numerical approximation of linear stochastic partial differential equations driven by additive noise. In the first part, we develop a framework for the analysis of weak convergence and within this framework we analyze the stochastic heat equation, the stochastic wave equation, and the linearized stochastic Cahn-Hilliard, or the linearized Cahn-Hilliard-Cook equation. READ MORE
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4. Exponential integrators for stochastic partial differential equations
Abstract : Stochastic partial differential equations (SPDEs) have during the past decades become an important tool for modeling systems which are influenced by randomness. Because of the complex nature of SPDEs, knowledge of efficient numerical methods with good convergence and geometric properties is of considerable importance. READ MORE
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5. Variational Methods for Moments of Solutions to Stochastic Differential Equations
Abstract : Numerical methods for stochastic differential equations typically estimate moments of the solution from sampled paths. Instead, we pursue the approach proposed by A. Lang, S. Larsson, and Ch. READ MORE