Predictability in Nonlinear Dynamical Systems with Model Uncertainty

TL;DR

This paper explores how stochastic differential equations and random dynamical systems can improve understanding of predictability in nonlinear systems affected by model uncertainty.

Contribution

It discusses techniques from random dynamical systems to analyze solution processes of stochastic differential equations in nonlinear systems with uncertainty.

Findings

Enhanced understanding of solution processes in stochastic nonlinear systems

Insights into predictability under model uncertainty

Potential methods for analyzing stochastic differential equations

Abstract

Nonlinear systems with model uncertainty are often described by stochastic differential equations. Some techniques from random dynamical systems are discussed. They are relevant to better understanding of solution processes of stochastic differential equations and thus may shed lights on predictability in nonlinear systems with model uncertainty.