Analysis of Stochastic Partial Differential Equations

We consider a forward-backward system of stochastic evolution equations in a Hilbert space. Under nondegeneracy assumptions on the diffusion coefficient (that may be nonconstant) we prove an analogue of the well-known Bismut-Elworthy formula. Next, we consider a nonlinear version of the Kolmogorov equation, i.e. a deterministic quasilinear equation associated to the system according to Pardoux, E and Peng, S. (1992). "Backward stochastic differential equations and quasilinear parabolic partial differential equations". In: Rozowskii, B.L., Sowers, R.B. (Eds.), Stochastic Partial Differential Equations and Their Applications , Lecture Notes in Control Inf. Sci., Vol. 176, pp. 200-217. Springer: Berlin. The Bismut-Elworthy formula is applied to prove smoothing effect, i.e. to prove existence and uniqueness of a solution which is differentiable with respect to the space variable, even if the initial datum and (some) coefficients of the equation are not. The results are then applied to the Hamilton-Jacobi-Bellman equation of stochastic optimal control. This way we are able to characterize optimal controls by feedback laws for a class of infinite-dimensional control systems, including in particular the stochastic heat equation with state-dependent diffusion coefficient.

In this paper we prove the existence of a unique solution for a class of stochastic parabolic partial differential equations in bounded domains, with Dirichlet boundary conditions. The main tool is an equivalence result, provided by the stochastic characteristics method, between the stochastic equations under investigation and a class of deterministic parabolic equations with moving boundaries, depending on random coefficients. We show the existence of the solution to this last problem, thus providing a solution to the former.

We consider a stochastic partial differential equation (SPDE) driven by a Lévy white noise, with Lipschitz multiplicative term σ. We prove that, under some conditions, this equation has a unique random field solution. These conditions are verified by the stochastic heat and wave equations. We introduce the basic elements of Malliavin calculus with respect to the compensated Poisson random measure associated with the Lévy white noise. If σ is affine, we prove that the solution is Malliavin differentiable and its Malliavin derivative satisfies a stochastic integral equation.

Dans ce papier, nous montrons un principe de comparaison trajectoriel pour l’equation de la chaleur stochastique, fractionnaire, nonlineaire sur $\mathbb{R}$ avec une donnee initiale a valeur mesure. Nous donnons des estimations quantitatives de la proximite a zero d’une solution. Ces resultats etendent le principe de comparaison de Mueller pour l’equation de la chaleur stochastique et permettent de considerer des donnees initiales plus generales telles que des mesures de Dirac et des mesures a queue plus lourde qu’une croissance exponentielle lineaire en ${\pm}\infty$. Ces resultats generalisent un travail recent par Moreno Flores (Ann. Probab. 42 (2014) 1635–1643), qui a prouve la stricte positivite de l’equation de la chaleur stochastique partant d’un Dirac. Comme application, nous etablissons la complete intermittence pour l’equation. Dans une etape intermediaire, nous prouvons la regularite Holder de solutions partant d’une donnee initiale a valeur mesure ce qui generalise, dans un certain sens, un travail recent de Chen and Dalang (Stoch. Partial Differ. Equ. Anal. Comput. 2 (2014) 316–352).

We prove the existence and uniqueness of global solutions to the semilinear stochastic heat equation on an unbounded spatial domain with forcing terms that grow superlinearly and satisfy an Osgood condition [Formula: see text] along with additional restrictions. For example, consider the forcing [Formula: see text]. A new dynamic weighting procedure is introduced to control the solutions, which are unbounded in space.

We give a new representation of fractional Brownian motion with Hurst parameter $H\leq\frac{1}{2}$ using stochastic partial differential equations. This representation allows us to use the Markov property and time reversal, tools which are not usually available for fractional Brownian motion. We then give simple proofs that fractional Brownian motion does not hit points in the critical dimension, and that it does not have double points in the critical dimension. These facts were already known, but our proofs are quite simple and use some ideas of Lévy. An Erratum is available in ECP volume 17 paper number 8 (http://dx.doi.org/10.1214/ECP.v17-1774)

Since Pardoux and Peng firstly studied the following nonlinear backward stochastic differential equations in 1990. The theory of BSDE has been widely studied and applied, especially in the stochastic control, stochastic differential games, financial mathematics and partial differential equations. In 1994, Pardoux and Peng came up with backward doubly stochastic differential equations to give the probabilistic interpretation for stochastic partial differential equations. Backward doubly stochastic differential equations theory has been widely studied because of its importance in stochastic partial differential equations and stochastic control problems. In this article, we will study the theory of doubly stochastic systems and related topics further.

In this paper, we propose a numerical method to solve the elliptic stochastic partial differential equations (SPDEs) obtained by Gaussian noises using an element free Galerkin method based on stabilized interpolating moving least square shape functions. The error estimates of the method is presented. The method is tested via several problems. The numerical results show the usefulness and accuracy of the new method.

We consider the stochastic heat equation of the following form: ∂ ∂ t u t ( x ) = ( L u t ) ( x ) + b ( u t ( x ) ) + σ ( u t ( x ) ) F ˙ t ( x ) for t > 0 , x ∈ R d , \begin{equation*} \frac {\partial }{\partial t}u_t(x) = (\mathcal {L} u_t)(x) +b(u_t(x)) + \sigma (u_t(x))\dot {F}_t(x)\quad \text {for }t>0,\ x\in \mathbf {R}^d, \end{equation*} where L \mathcal {L} is the generator of a Lévy process and F ˙ \dot {F} is a spatially-colored, temporally white, Gaussian noise. We will be concerned mainly with the long-term behavior of the mild solution to this stochastic PDE. For the most part, we work under the assumptions that the initial data u 0 u_0 is a bounded and measurable function and σ \sigma is nonconstant and Lipschitz continuous. In this case, we find conditions under which the preceding stochastic PDE admits a unique solution which is also weakly intermittent. In addition, we study the same equation in the case where L u \mathcal {L}u is replaced by its massive/dispersive analogue L u − λ u \mathcal {L}u-\lambda u , where λ ∈ R \lambda \in \mathbf {R} . We also accurately describe the effect of the parameter λ \lambda on the intermittence of the solution in the case where σ ( u ) \sigma (u) is proportional to u u [the “parabolic Anderson model”]. We also look at the linearized version of our stochastic PDE, that is, the case where σ \sigma is identically equal to one [any other constant also works]. In this case, we study not only the existence and uniqueness of a solution, but also the regularity of the solution when it exists and is unique.

A brief discussion on the relevance of stochastic partial differential equations (SPDEs) in Sect. 9.1 is followed by a review of the type of SPDEs studied in the mathematical literature (Sect. 9.2). Section 9.3 shows that SPDEs can be solved by the methods in Chaps. 7 and 8 via time and space discretization. Section 9.4 deals with SPDEs that are frequently encountered in applications. The focus is on stochastic elliptic partial differential equations. We review general concepts on this class of equations (Sect. 9.4.1), and present solutions by Monte Carlo (Sect. 9.4.4), stochastic reduced order models (Sect. 9.4.5), stochastic Galerkin (Sect. 9.4.8), and stochastic collocation (Sect. 9.4.9) methods. The last section of the chapter considers SDEs whose coefficients have small uncertainty, and presents methods for solving these equations by Taylor (Sect. 9.5.1), perturbation (Sect. 9.5.2), and Neumann series (Sect. 9.5.3) representations.KeywordsRandom FieldCollocation PointRandom ElementRandom CoefficientStochastic Partial Differential EquationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

A Study on Uncertainty of Discharge in River Channel Using Stochastic Partial Differential Equation

The main goal of this paper is to prove that there are solutions to stochastic Burgers type equations that have an oblique reflection direction. For penalized systems of stochastic partial differential equations (SPDEs), we use approximations. The method makes use of approximations of SPDE penalized systems. We establish the existence of solutions for stochastic Burgers type heat equations with oblique reflection by using a priori estimates for the solutions of the penalized systems of SPDEs.

A noncooperative, two person, zero sum, stochastic differential game is formulated and solved that is described by a linear stochastic equation in a Hilbert space with a fractional Brownian motion and a quadratic payoff functional for the two players. The stochastic equation can model stochastic partial differential equations not only with distributed strategies and noise but also with control strategies and noise restricted to the boundary of the domain. The optimal strategies for the two players are given explicitly. The verification method is a generalization of completion of squares and provides the optimal strategies directly without solving partial differential equations or backward stochastic differential equations. Some examples of games described by stochastic partial differential equations are given.

In this work, we develop a method for dynamic output feedback covariance control of the state covariance of linear dissipative stochastic partial differential equations (PDEs) using spatially distributed control actuation and sensing with noise. Such stochastic PDEs arise naturally in the modeling of surface height profile evolution in thin film growth and sputtering processes. We begin with the formulation of the stochastic PDE into a system of infinite stochastic ordinary differential equations (ODEs) by using modal decomposition. A finite-dimensional approximation is then obtained to capture the dominant mode contribution to the surface roughness profile (i.e., the covariance of the surface height profile). Subsequently, a state feedback controller and a Kalman-Bucy filter are designed on the basis of the finite-dimensional approximation. The dynamic output feedback covariance controller is subsequently obtained by combining the state feedback controller and the state estimator. The steady-state expected surface covariance under the dynamic output feedback controller is then estimated on the basis of the closed-loop finite-dimensional system. An analysis is performed to obtain a theoretical estimate of the expected surface covariance of the closed-loop infinite-dimensional system. Applications of the linear dynamic output feedback controller to the linearized stochastic Kuramoto-Sivashinsky equation are presented.

The aim of this paper is to investigate the existence of optimal controls for systems described by stochastic partial differential equations (SPDEs) with locally monotone coefficients controlled by external forces which are feedback controls. To attain our objective we adapt the argument of Lisei (2002) where the existence of optimal controls to the stochastic Navier–Stokes equation was studied. The results obtained in the present paper may be applied to demonstrate the existence of optimal controls to various types of controlled SPDEs such as: a stochastic nonlocal equation and stochastic semilinear equations which are locally monotone equations; we also apply the result to a monotone equation such as the stochastic reaction–diffusion equation and to a stochastic linear equation.