We consider stochastic dynamical systems with multiple time scales. An intermediate reduced model is obtained and explored for a slow-fast system when the fast mode is driven by white noise. First, and for later comparison, one approximation to the reduced stochastic system on the exponentially attracting stochastic slow manifold is derived to errors of order O(ε). Second, because the noise only drives the fast modes, averaging derives an autonomous deterministic system with errors of order O(ε). Then a martingale argument accounts for fluctuations about the averaged system to form an intermediate reduced model with errors of order O(ε)-the autonomous deterministic system now driven by white noise. This intermediate reduced model has a simp...
We present, in the simplest possible form, the so called {\em martingale problem} strategy to establ...
AbstractAsymptotic problems for classical dynamical systems, stochastic processes, and PDEs can lead...
We study a variance reduction strategy based on control variables for simulating the averaged macros...
We prove a stochastic averaging theorem for stochastic differential equations in which the slow and ...
We consider slow-fast systems of differential equations, in which both the slow and fast variables a...
AbstractWe consider slow–fast systems of differential equations, in which both the slow and fast var...
Eliminate the fast degrees of freedom in multiscale systems and derive an effective (stochastic) mod...
Macroscopic reduction methods, such as averaging, homogenization and slow manifold approximation, ha...
Averaging is an important method to extract effective macroscopic dynamics from complex systems with...
Averaging and homogenization workshop, Luminy. Fast-slow systems We consider fast-slow systems of th...
We present a novel characterization of slow variables for continuous Markov processes that provably ...
We analyze the effect of additive fractional noise with Hurst parameter H>1/2 on fast-slow systems. ...
In this work we use the stochastic flow decomposition technique to get components that represent the...
An averaged system to approximate the slow dynamics of a two timescale nonlinear stochastic control ...
Summary. This article is concerned with stochastic differential equations with dis-parate temporal s...
We present, in the simplest possible form, the so called {\em martingale problem} strategy to establ...
AbstractAsymptotic problems for classical dynamical systems, stochastic processes, and PDEs can lead...
We study a variance reduction strategy based on control variables for simulating the averaged macros...
We prove a stochastic averaging theorem for stochastic differential equations in which the slow and ...
We consider slow-fast systems of differential equations, in which both the slow and fast variables a...
AbstractWe consider slow–fast systems of differential equations, in which both the slow and fast var...
Eliminate the fast degrees of freedom in multiscale systems and derive an effective (stochastic) mod...
Macroscopic reduction methods, such as averaging, homogenization and slow manifold approximation, ha...
Averaging is an important method to extract effective macroscopic dynamics from complex systems with...
Averaging and homogenization workshop, Luminy. Fast-slow systems We consider fast-slow systems of th...
We present a novel characterization of slow variables for continuous Markov processes that provably ...
We analyze the effect of additive fractional noise with Hurst parameter H>1/2 on fast-slow systems. ...
In this work we use the stochastic flow decomposition technique to get components that represent the...
An averaged system to approximate the slow dynamics of a two timescale nonlinear stochastic control ...
Summary. This article is concerned with stochastic differential equations with dis-parate temporal s...
We present, in the simplest possible form, the so called {\em martingale problem} strategy to establ...
AbstractAsymptotic problems for classical dynamical systems, stochastic processes, and PDEs can lead...
We study a variance reduction strategy based on control variables for simulating the averaged macros...