Modeling Fast Diffusion Processes in Time Integration of Stiff Stochastic Differential Equations

Numerical algorithms for stiff stochastic differential equations are developed using lin-ear approximations of the fast diffusion processes,under the assumption of decoupling between fast and slow processes.Three numerical schemes are proposed,all of which are based on the linearized formulation albeit with different degrees of approximation.The schemes are of comparable complexity to the classical explicit Euler-Maruyama scheme but can achieve better accuracy at larger time steps in stiff systems.Convergence analysis is conducted for one of the schemes,that shows it to have a strong convergence order of 1/2 and a weak convergence order of 1.Approximations arriving at the other two schemes are discussed.Numerical experiments are carried out to examine the convergence of the schemes proposed on model problems.

Some Ergodic Theorems for a Parabolic Anderson Model

In this paper, we study some ergodic theorems of a class of linear systems of interacting diffusions, which is a parabolic Anderson model. First, under the assumption that the transition kernel a = （a（i,j））i,j∈s is doubly stochastic, we obtain the long-time convergence to an invariant probability measure Vh starting from a bounded a-harmonic function h based on self-duality property, and then we show the convergence to the invariant probability measure Uh holds for a broad class of initial distributions. Second, if （a（i, j））i,j∈s is transient and symmetric, and the diffusion parameter c remains below a threshold, we are able to determine the set of extremal invariant probability measures with finite second moment. Finally, in the case that the transition kernel （a（i,j））i,j∈s is doubly stochastic and satisfies Case I （see Case I in [Shiga, T.： An interacting system in population genetics. J. Math. Kyoto Univ., 20, 213-242 （1980）]）, we show that this parabolic Anderson model locally dies out independent of the diffusion parameter c.