Macroscopic reduction methods, such as averaging, homogenization and slow manifold approximation, have been proposed for stochastic partial differential equations (SPDEs) with separated time and/or spatial scales in recent years. Here we overview some very recent results of applying these methods to derive effective reduced models for stochastic partial differential equations.Jinqiao Duan, Anthony Roberts, and Wei Wan
In this work we use the stochastic flow decomposition technique to get components that represent the...
These lectures concentrate on (nonlinear) stochastic partial differential equations (SPDE) of evolut...
In Holm (Holm 2015 Proc. R. Soc. A 471, 20140963. (doi:10.1098/rspa.2014.0963)), stochastic fluid eq...
In this second volume, a general approach is developed to provide approximate parameterizations of t...
We consider stochastic dynamical systems with multiple time scales. An intermediate reduced model is...
The macroscopic behavior of dissipative stochastic partial differential equations usually can be des...
Averaging is an important method to extract effective macroscopic dynamics from complex systems with...
International audienceThis article is devoted to the analysis of semilinear, parabolic, Stochastic P...
Liu W, Röckner M, Sun X, Xie Y. Strong Averaging Principle for Slow-Fast Stochastic Partial Differen...
Abstract. Part I of this article is devoted to the leading order approximations of stochastic critic...
We present a comparative study of two methods for the reduction of the dimensionality of a system o...
A stochastic partial differential equation (SPDE) is a partial differential equation containing a ra...
We prove a stochastic averaging theorem for stochastic differential equations in which the slow and ...
Abstract The main goal of this work is to study an averaging principle for two-time-scales stochasti...
The computer algebra routines documented here empower you to reproduce and check many of the details...
In this work we use the stochastic flow decomposition technique to get components that represent the...
These lectures concentrate on (nonlinear) stochastic partial differential equations (SPDE) of evolut...
In Holm (Holm 2015 Proc. R. Soc. A 471, 20140963. (doi:10.1098/rspa.2014.0963)), stochastic fluid eq...
In this second volume, a general approach is developed to provide approximate parameterizations of t...
We consider stochastic dynamical systems with multiple time scales. An intermediate reduced model is...
The macroscopic behavior of dissipative stochastic partial differential equations usually can be des...
Averaging is an important method to extract effective macroscopic dynamics from complex systems with...
International audienceThis article is devoted to the analysis of semilinear, parabolic, Stochastic P...
Liu W, Röckner M, Sun X, Xie Y. Strong Averaging Principle for Slow-Fast Stochastic Partial Differen...
Abstract. Part I of this article is devoted to the leading order approximations of stochastic critic...
We present a comparative study of two methods for the reduction of the dimensionality of a system o...
A stochastic partial differential equation (SPDE) is a partial differential equation containing a ra...
We prove a stochastic averaging theorem for stochastic differential equations in which the slow and ...
Abstract The main goal of this work is to study an averaging principle for two-time-scales stochasti...
The computer algebra routines documented here empower you to reproduce and check many of the details...
In this work we use the stochastic flow decomposition technique to get components that represent the...
These lectures concentrate on (nonlinear) stochastic partial differential equations (SPDE) of evolut...
In Holm (Holm 2015 Proc. R. Soc. A 471, 20140963. (doi:10.1098/rspa.2014.0963)), stochastic fluid eq...