We prove a stochastic averaging theorem for stochastic differential equations in which the slow and the fast variables interact. The approximate Markov fast motion is a family of Markov process with generator Lx for which we obtain a quantitative locally uniform law of large numbers and obtain the continuous dependence of their invariant measures on the parameter x. These results are obtained under the assumption that Lx satisfies Hörmander’s bracket conditions, or more generally Lx is a family of Fredholm operators with sub-elliptic estimates. For stochastic systems in which the slow and the fast variable are not separate, conservation laws are essential ingredients for separating the scales in singular perturbation problems we demonstrate...
We aim to extend the weak averaging principle for the multiscale systems driven by Gaussian noises i...
This article is concerned with the averaging principle and its extensions for stochastic dynamical s...
AbstractAveraging is an important method to extract effective macroscopic dynamics from complex syst...
We consider stochastic dynamical systems with multiple time scales. An intermediate reduced model is...
We consider slow-fast systems of differential equations, in which both the slow and fast variables a...
AbstractAsymptotic problems for classical dynamical systems, stochastic processes, and PDEs can lead...
AbstractWe consider slow–fast systems of differential equations, in which both the slow and fast var...
We study the validity of an averaging principle for a slow-fast system of stochastic reaction-diffus...
Consider a stochastic differential equation whose diffusion vector fields are formed from an integra...
In this work we use the stochastic flow decomposition technique to get components that represent the...
In this paper we prove the strong averaging principle for a slow-fast system of rough differential e...
We investigate the effective behaviour of a small transversal perturbation of order epsilon to a com...
Liu W, Röckner M, Sun X, Xie Y. Averaging principle for slow-fast stochastic differential equations ...
AbstractThe theory of stochastic averaging principle provides an effective approach for the qualitat...
In this work we are concerned with the study of the strong order of convergence in the averaging pri...
We aim to extend the weak averaging principle for the multiscale systems driven by Gaussian noises i...
This article is concerned with the averaging principle and its extensions for stochastic dynamical s...
AbstractAveraging is an important method to extract effective macroscopic dynamics from complex syst...
We consider stochastic dynamical systems with multiple time scales. An intermediate reduced model is...
We consider slow-fast systems of differential equations, in which both the slow and fast variables a...
AbstractAsymptotic problems for classical dynamical systems, stochastic processes, and PDEs can lead...
AbstractWe consider slow–fast systems of differential equations, in which both the slow and fast var...
We study the validity of an averaging principle for a slow-fast system of stochastic reaction-diffus...
Consider a stochastic differential equation whose diffusion vector fields are formed from an integra...
In this work we use the stochastic flow decomposition technique to get components that represent the...
In this paper we prove the strong averaging principle for a slow-fast system of rough differential e...
We investigate the effective behaviour of a small transversal perturbation of order epsilon to a com...
Liu W, Röckner M, Sun X, Xie Y. Averaging principle for slow-fast stochastic differential equations ...
AbstractThe theory of stochastic averaging principle provides an effective approach for the qualitat...
In this work we are concerned with the study of the strong order of convergence in the averaging pri...
We aim to extend the weak averaging principle for the multiscale systems driven by Gaussian noises i...
This article is concerned with the averaging principle and its extensions for stochastic dynamical s...
AbstractAveraging is an important method to extract effective macroscopic dynamics from complex syst...