Approximating the Density of Random Differential Equations with Weak Nonlinearities via Perturbation Techniques

Abstract

We combine the stochastic perturbation method with the maximum entropy principle to construct approximations of the first probability density function of the steady-state solution of a class of nonlinear oscillators subject to small perturbations in the nonlinear term and driven by a stochastic excitation. The nonlinearity depends both upon position and velocity, and the excitation is given by a stationary Gaussian stochastic process with certain additional properties. Furthermore, we approximate higher-order moments, the variance, and the correlation functions of the solution. The theoretical findings are illustrated via some numerical experiments that confirm that our approximations are reliable.

Keywords

• stochastic perturbations
• random nonlinear oscillator
• maximum entropy principle
• probability density function
• stationary Gaussian noise