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Large deviation principle (LDP) for stochastic differential equation is one of the interesting and modern topics in stochastic analysis. Principally, this theory gives the rate of convergence to the solution of the corresponding deterministic equation when the noise tends to zero. The study of LDP for SDE’s has been initiated by M. Freidlin and A. Wentzell and then has been considered by many other researchers. Freidlin andWentzell divided the interval [0, T] to small subintervals and considered the diffusion coefficient as a constant on any small subintervals. Then the problem is reduced to the additive noise case. But using the contraction principle, the study of LDP for an equation with... 

Surface growth have been one of the most interesting topics of research in non-equilibrium Statistical physics, due to their relevance in studying industrial growth processes. Many models such as Edwards-Wilkinson and KPZ have been proposed to study these systems where by incorporating renormalization group, numerical integration and computer simulations we can derive their critical exponents. In general, a thermal noise is implemented in these models, however, other types can be used as well. In particular for the case of Edwards-Wilkinson, it has been shown that a multiplicative noise changes the universality class of the model. In this thesis we want to investigate the effects of... 

We establish the existence and uniqueness of the mild solution for stochastic Volterra equation with a non-self-adjoint operator. The specific Volterra equation that we consider is a generalization of the fractional differential equation. To obtain the mild solution for the case of multiplicative problem, the resolvent property of the linear perturbation of a sectorial operator will be considered. Moreover, we establish the existence and uniqueness of the mild solution for semilinear stochastic Volterra equation involving a demicontinuous and semimonotone nonlinearity. The Volterra equation in this case, has a positive-type memory kernel. To obtain the mild solution of the multiplicative... 

In this thesis we study Galerkin methods for semilinear stochastic partial differential equations (SPDEs) with multiplicative noise and Lipschitz continuous nonlinearities. The strong error of convergence for spatially semidiscrete approximations as well as a spatio-temporal discretization which is based on a linear implicit Euler–Maruyama method, are also investigated. We see that the obtained error estimates in both cases as well as the regularity results for the mild solution of the SPDE are optimal. The results hold for different Galerkin methods such as the standard finite element method or spectral Galerkin. At the end, these theoretical findings are accompanied by several numerical...