Predictor-corrector methods for a linear stochastic oscillator with additive noise

Authors:
Jialin Hong;Rudolf Scherer;Lijin Wang
Affiliations:
State Key Laboratory of Scientific and Engineering Computing, Institute of Computational Mathematics and Scientific/Engineering Computing, Academy of Mathematics and Systems Science, Chinese Acade ...;Universitaet Karlsruhe (TH), Institute for Applied and Numerical Mathematics, 76128 Karlsruhe, Germany;State Key Laboratory of Scientific and Engineering Computing, Institute of Computational Mathematics and Scientific/Engineering Computing, Academy of Mathematics and Systems Science, Chinese Acade ...
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
2007

Citing 5
Cited 2

Symplectic Integration of Hamiltonian Systems with Additive Noise

SIAM Journal on Numerical Analysis
Strong Convergence of Euler-Type Methods for Nonlinear Stochastic Differential Equations

SIAM Journal on Numerical Analysis
Numerical Methods for Stochastic Systems Preserving Symplectic Structure

SIAM Journal on Numerical Analysis
An Algorithmic Introduction to Numerical Simulation of Stochastic Differential Equations

SIAM Review
Numerical simulation of a linear stochastic oscillator with additive noise

Applied Numerical Mathematics

Qualitative analysis and simulation of a 10 degrees of freedom model of an electrically operated vehicle

Computers & Mathematics with Applications
Original articles: On the numerical discretisation of stochastic oscillators

Mathematics and Computers in Simulation

Quantified Score

Hi-index	0.98

Visualization

Abstract

The predictor-corrector methods P(EC)^k with equidistant discretization are applied to the numerical integration of a linear stochastic oscillator. Their ability in preserving the symplecticity, the linear growth property of the second moment, and the oscillation property of the solution of this stochastic system is studied. Their mean-square orders of convergence are discussed. Numerical experiments are performed.