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This paper aims to investigate the weak convergence of the Rosenbrock semi-implicit method for semilinear parabolic stochastic partial differential equations (SPDEs) driven by additive noise. We are interested in SPDEs where the nonlinear part is stronger than the linear part, also called stochastic reaction dominated transport equations. For such SPDEs, many standard numerical schemes lose their stability properties. Exponential Rosenbrock and Rosenbrock-type methods were proved to be efficient for such SPDEs, but only their strong convergence were recently analyzed. Here, we investigate the weak convergence of the Rosenbrock semi-implicit method. We obtain a weak convergence rate which is twice the rate of the strong convergence. Our error analysis does not rely on Malliavin calculus, but rather only uses the Kolmogorov equation and the smoothing properties of the resolvent operator resulting from the Rosenbrock semi-implicit approximation. [ABSTRACT FROM AUTHOR]

In this article, we present a new class of semi‐Lagrangian methods for the numerical approximation of hyperbolic conservation laws. The methods provide a strategy for resolving nonlinear hyperbolic conservations laws within the framework of semi‐Lagrangian discontinuous Galerkin finite‐element methods (SLDG) initially proposed for linear advection problems [J‐M. Qiu and C‐W. Shu, J Comput Phys 230 (2011)]. Using a flux‐correction technique we combine the good numerical properties of the SLDG methods with those of the well‐known Runge‐Kutta discontinuous Galerkin methods (RKDG). The high‐order accuracy and local conservative properties of the resulting schemes make them very attractive for convection‐dominated convection‐diffusion problems, and conservation laws in particular. The semi‐Lagrangian component of the schemes allows for the use of larger Courant numbers than is possible when using high‐order RKDG methods alone. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the numerical approximation of stochastic convection–reaction–diffusion equations using two explicit exponential integrators. The stochastic partial differential equation (SPDE) is driven by additive Wiener process. The approximation in space is done via a combination of the standard finite element method and the Galerkin projection method. Using the linear functional of the noise, we construct two accelerated numerical methods, which achieve higher convergence orders. In particular, we achieve convergence rates approximately 1 for trace class noise and 12 for space‐time white noise. These convergence orders are obtained under less regularity assumptions on the nonlinear drift function than those used in the literature for stochastic reaction–diffusion equations. Numerical experiments to illustrate our theoretical results are provided. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the numerical approximation of a general second order parabolic stochastic partial differential equation(SPDE) driven by multiplicative and additive noise. Our main interest is on such SPDEs where the nonlinear part is stronger than the linear part, usually called stochastic dominated transport equations. Most standard numerical schemes lose their good stability properties on such equations, including the current linear implicit Euler method. We discretize the SPDE in space by the finite element method and propose a novel scheme called stochastic Rosenbrock-type scheme for temporal discretization. Our scheme is based on the local linearization of the semi-discrete problem obtained after space discretization and is more appropriate for such equations. We provide a strong convergence of the new fully discrete scheme toward the exact solution for multiplicative and additive noise and obtain optimal rates of convergence. Numerical experiments to sustain our theoretical results are provided. [ABSTRACT FROM AUTHOR]

This paper aims to investigate numerical approximation of a general second order non-autonomous semilinear parabolic stochastic partial differential equation (SPDEs) driven by multiplicative noise. Numerical approximations of autonomous SPDEs are thoroughly investigated in the literature, while the non-autonomous case is not yet understood. We discretize the non-autonomous SPDE by the finite element method in space and the Magnus-type integrator in time. We provide a strong convergence proof of the fully discrete scheme toward the mild solution in the root-mean-square L2 norm. The result reveals how the convergence orders in both space and time depend on the regularity of the noise and the initial data. In particular, for multiplicative trace class noise we achieve convergence order O(h2(1 + max(0,ln(tm/h2))+Δt½). Numerical simulations to illustrate our theoretical finding are provided. [ABSTRACT FROM AUTHOR]

In this work, numerical approximation of the second order non-autonomous semilinear parabolic partial differential equations (PDEs) is investigated using the classical finite element method. To the best of our knowledge, only the linear case is investigated in the literature. Using an approach based on evolution operator depending on two parameters, we obtain the error estimate of the semi-discrete scheme based on finite element method toward the mild solution of semilinear non-autonomous PDEs under polynomial growth and one-sided Lipschitz conditions of the nonlinear term. Our convergence rate is obtained with general non-smooth initial data, and is similar to that of the autonomous case. Such convergence result is very important in numerical analysis. For instance, it is one step forward for numerical approximation of non-autonomous stochastic partial differential equations with the finite element method. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the numerical approximation of semilinear non-autonomous stochastic partial differential equations (SPDEs) driven by multiplicative or additive noise. Such equations are more realistic than the autonomous equations when modelling real world phenomena. Such equations find applications in many fields such as transport in porous media, quantum fields theory, electromagnetism and nuclear physics. Numerical approximations of autonomous SPDEs are thoroughly investigated in the literature, while the non-autonomous case are not yet well understood. Here, a non-autonomous SPDE is discretized in space by the finite element method and in time by the linear implicit Euler method. We break the complexity in the analysis of the time depending, not necessarily self-adjoint linear operators with the corresponding semi group and provide the strong convergence result of the fully discrete scheme toward the mild solution. The results indicate how the converge orders depend on the regularity of the initial solution and the noise. Additionally, for additive noise we achieve convergence order in time approximately 1 under less regularity assumptions on the nonlinear drift term than required in the current literature, even in the autonomous case. Numerical simulations motivated from realistic porous media flow are provided to illustrate our theoretical finding. [ABSTRACT FROM AUTHOR]

Abstract This paper deals with the numerical approximation of semilinear parabolic stochastic partial differential equation (SPDE) driven simultaneously by Gaussian noise and Poisson random measure, more realistic in modeling real world phenomena. The SPDE is discretized in space with the standard finite element method and in time with the linear implicit Euler method or an exponential integrator, more efficient and stable for stiff problems. We prove the strong convergence of the fully discrete schemes toward the mild solution. The results reveal how convergence orders depend on the regularity of the noise and the initial data. In addition, we exceed the classical orders 1 ∕ 2 in time and 1 in space achieved in the literature when dealing with SPDE driven by Poisson measure with less regularity assumptions on the nonlinear drift function. In particular, for trace class multiplicative Gaussian noise we achieve convergence order O (h 2 + Δ t 1 ∕ 2). For additive trace class Gaussian noise and an appropriate jump function, we achieve convergence order O (h 2 + Δ t). Numerical experiments to sustain the theoretical results are provided. [ABSTRACT FROM AUTHOR]

Abstract This paper aims to investigate the numerical approximation of a general second order parabolic stochastic partial differential equation(SPDE) driven by multiplicative or additive noise. The linear operator is not necessary self-adjoint, so more useful in concrete applications. The SPDE is discretized in space by the finite element method and in time by the linear implicit Euler method. The corresponding scheme is more stable and efficient to solve stochastic advection-dominated reactive transport in porous media. This extends the current results in the literature to not necessary self-adjoint operator. As a challenge, key part of the proof does not rely anymore on the spectral decomposition of the linear operator. The results reveal how the convergence orders depend on the regularity of the noise and the initial data. In particular for multiplicative trace class noise, we achieve optimal convergence order O (h 2 + Δ t 1 / 2) and for additive trace class noise, we achieve optimal convergence order in space and sup-optimal convergence order in time of the form O (h 2 + Δ t 1 − ϵ) , for an arbitrarily small ϵ > 0. Numerical experiments to sustain our theoretical results are provided. [ABSTRACT FROM AUTHOR]

Abstract We consider the numerical approximation of the general second order semilinear parabolic stochastic partial differential equations (SPDEs) driven by additive space–time noise. Our goal is to build two numerical algorithms with strong convergence rates higher than that of the standard semi-implicit scheme. In contrast to the standard time stepping methods which use basic increments of the noise, we introduce two schemes based on the exponential integrators, designed for finite element, finite volume or finite difference space discretisations. We prove the convergence in the root mean square L 2 norm for a general advection diffusion reaction equation and a family of new Lipschitz nonlinearities. We observe from both the analysis and numerics that the proposed schemes have better convergence properties than the current standard semi-implicit scheme. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we consider the numerical approximation of a general second order semilinear parabolic partial differential equation. Equations of this type arise in many contexts, such as transport in porous media. Using finite element method for space discretization and the exponential Rosenbrock–Euler method for time discretization, we provide a convergence proof in space and time under only the standard Lipschitz condition of the nonlinear part, for both smooth and nonsmooth initial solutions. This is in contrast to restrictive assumptions made in the literature, where the authors have considered only approximation in time so far in their convergence proofs. The main result reveals how the convergence orders in both space and time depend heavily on the regularity of the initial data. In particular, the method achieves optimal convergence order O h 2 + Δ t 2 when the initial data belongs to the domain of the linear operator. Numerical simulations to sustain our theoretical result are provided. [ABSTRACT FROM AUTHOR]

We consider the numerical approximation of a general second order semi–linear parabolic stochastic partial differential equation (SPDE) driven by additive space-time noise. We introduce a new modified scheme using linear functionals of the noise with the semi–implicit Euler–Maruyama method in time, and the finite element method in space (although extension to finite differences or finite volumes would be possible). We prove the convergence in the root mean square L 2 norm for a diffusion reaction equation and diffusion advection reaction equation with a large family of Lipschitz nonlinear functions. We present numerical results for a linear reaction diffusion equation in two dimensions as well as a nonlinear example of two-dimensional stochastic advection diffusion reaction equation. We observe from both the analysis and numerics that the proposed scheme has better convergence properties than the standard semi–implicit Euler–Maruyama method. [ABSTRACT FROM AUTHOR]

In this paper, we consider the numerical approximation of a general second order semilinear stochastic spartial differential equation (SPDE) driven by multiplicative and additive noise. Our main interest is on such SPDEs where the nonlinear part is stronger than the linear part also called stochastic reactive dominated transport equations. Most numerical techniques, including current stochastic exponential integrators lose their good stability properties on such equations. Using finite element for space discretization, we propose a new scheme appropriated on such equations, called stochastic exponential Rosenbrock scheme based on local linearization at every time step of the semi-discrete equation obtained after space discretization. We consider noise with finite trace and give a strong convergence proof of the new scheme toward the exact solution in the root-mean-square L2 norm. Numerical experiments to sustain theoretical results are provided. [ABSTRACT FROM AUTHOR]

We consider the linearized Crocco equation in fluid dynamics with incomplete data and Robin boundary conditions, and address theoretical and numerical distributed null control. The controllability problem is solved through a dual reformulation. We first resolve some constrained extremal problems and apply appropriated duality techniques that lead to the formulation of equivalent unconstrained extremal problem in variational form. Novel numerical technique based on finite difference-finite element space discretization and exponential integrator in time discretization is proposed. For illustration, numerical simulations are provided. [ABSTRACT FROM AUTHOR]

We consider a finite element approximation of a general semi-linear stochastic partial differential equation (SPDE) driven by space-time multiplicative and additive noise. We examine the full weak convergence rate of the exponential Euler scheme when the linear operator is self adjoint and also provide the full weak convergence rate for non-self-adjoint linear operator with additive noise. Key part of the proof does not rely on Malliavin calculus. For non-self-adjoint operators, we analyse the optimal strong error for spatially semi-discrete approximations for both multiplicative and additive noise with truncated and non-truncated noise. Depending on the regularity of the noise and the initial solution, we found that in some cases the rate of weak convergence is twice the rate of the strong convergence. Our convergence rate is in agreement with some numerical results in two dimensions. [ABSTRACT FROM AUTHOR]

We consider the numerical approximation of a general second-order semilinear parabolic stochastic partial differential equation driven by multiplicative and additive space–time noise. We examine convergence of exponential integrators for multiplicative and additive noise. We consider noise that is in a trace class and give a convergence proof in the root-mean-square L2 norm. We discretize in space with the finite element method and in our implementation we examine both the finite element and the finite volume methods. We present results for a linear reaction–diffusion equation in two dimensions as well as a nonlinear example of a two-dimensional stochastic advection–diffusion–reaction equation motivated from realistic porous media flow. [ABSTRACT FROM PUBLISHER]