Information Closure Method for Dynamic Analysis of Nonlinear Stochastic Systems

Abstract

An information closure method for analytical investigation of response statistics and robust stability of nonlinear stochastic dynamic systems is proposed. Entropy modes are defined first based on the decomposition of probability density functions estimated by maximizing entropy in quasi-stationary. Then the entropy modes are selected and employed in the moment equations as the constraints for information closure. The estimated density with Lagrange multipliers is used for the closure of the hierarchical moment equations. By selecting single independent mode in every state, an explicit analysis of the entropy and density function can be obtained. The performance of the closure method is supported by employing three stochastic systems with some stationary exact solutions and through Monte Carlo simulations.