ﻻ يوجد ملخص باللغة العربية
In this paper we solve a selection problem for multidimensional SDE $d X^varepsilon(t)=a(X^varepsilon(t)) d t+varepsilon sigma(X^varepsilon(t)), d W(t)$, where the drift and diffusion are locally Lipschitz continuous outside of a fixed hyperplane $H$. It is assumed that $X^varepsilon(0)=x^0in H$, the drift $a(x)$ has a Hoelder asymptotics as $x$ approaches $H$, and the limit ODE $d X(t)=a(X(t)), d t$ does not have a unique solution. We show that if the drift pushes the solution away of $H$, then the limit process with certain probabilities selects some extreme solutions to the limit ODE. If the drift attracts the solution to $H$, then the limit process satisfies an ODE with some averaged coefficients. To prove the last result we formulate an averaging principle, which is quite general and new.
We study the limit behavior of differential equations with non-Lipschitz coefficients that are perturbed by a small self-similar noise. It is proved that the limiting process is equal to the maximal solution or minimal solution with certain probabili
We study ODEs with vector fields given by general Schwartz distributions, and we show that if we perturb such an equation by adding an infinitely regularizing path, then it has a unique solution and it induces an infinitely smooth flow of diffeomorph
We consider the $[0,1]$-valued solution $(u_{t,x}:tgeq 0, xin mathbb R)$ to the one dimensional stochastic reaction diffusion equation with Wright-Fisher noise [ partial_t u = partial_x^2 u + f(u) + epsilon sqrt{u(1-u)} dot W. ] Here, $W$ is a space-
In this paper we study zero-noise limits of $alpha -$stable noise perturbed ODEs which are driven by an irregular vector field $A$ with asymptotics $% A(x)sim overline{a}(frac{x}{leftvert xrightvert })leftvert xrightvert ^{beta -1}x$ at zero, where $
The problem on identification of a limit of an ordinary differential equation with discontinuous drift that perturbed by a zero-noise is considered in multidimensional case. This problem is a classical subject of stochastic analysis. However the mult