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

DOI10.1016/j.mcm.2006.12.009zbMath1140.65014OpenAlexW2070812878MaRDI QIDQ2467150

Jialin Hong, Rudolf Scherer, Lijin Wang

Publication date: 18 January 2008

Published in: Mathematical and Computer Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.mcm.2006.12.009

zbMATH Keywords

Numerical experimentsSecond momentPredictor-corrector methodSymplecticityLinear stochastic oscillatorMean-square convergenceStochastic Hamiltonian system

Mathematics Subject Classification ID

Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Stability and convergence of numerical methods for ordinary differential equations (65L20) Ordinary differential equations and systems with randomness (34F05) Multistep, Runge-Kutta and extrapolation methods for ordinary differential equations (65L06) Generation, random and stochastic difference and differential equations (37H10) Computational methods for stochastic equations (aspects of stochastic analysis) (60H35) Numerical solutions to stochastic differential and integral equations (65C30) Numerical methods for Hamiltonian systems including symplectic integrators (65P10) Discretization methods and integrators (symplectic, variational, geometric, etc.) for dynamical systems (37M15)

Related Items (15)

Stochastic symplectic Runge-Kutta methods for the strong approximation of Hamiltonian systems with additive noise ⋮ Generating functions for stochastic symplectic methods ⋮ Integration of the stochastic underdamped harmonic oscillator by the \(\theta \)-method ⋮ Unnamed Item ⋮ Qualitative analysis and simulation of a 10 degrees of freedom model of an electrically operated vehicle ⋮ Convergence analysis of trigonometric methods for stiff second-order stochastic differential equations ⋮ Mean-square convergence analysis of the semi-implicit scheme for stochastic differential equations driven by the Wiener processes ⋮ On the numerical discretisation of stochastic oscillators ⋮ Preservation of quadratic invariants of stochastic differential equations via Runge-Kutta methods ⋮ On the numerical integration of the undamped harmonic oscillator driven by independent additive Gaussian white noises ⋮ Long-term adaptive symplectic numerical integration of linear stochastic oscillators driven by additive white noise ⋮ Locally linearized methods for the simulation of stochastic oscillators driven by random forces ⋮ Projection methods for stochastic differential equations with conserved quantities ⋮ Stochastic symplectic methods based on the Padé approximations for linear stochastic Hamiltonian systems ⋮ A review on numerical schemes for solving a linear stochastic oscillator

Cites Work

• Stochastic oscillators
• Higher-order implicit strong numerical schemes for stochastic differential equations
• Numerical simulation of a linear stochastic oscillator with additive noise
• An Algorithmic Introduction to Numerical Simulation of Stochastic Differential Equations
• The Invariance of Asymptotic Laws of Linear Stochastic Systems under Discretization
• Symplectic Integration of Hamiltonian Systems with Additive Noise
• Strong Convergence of Euler-Type Methods for Nonlinear Stochastic Differential Equations
• Numerical Methods for Stochastic Systems Preserving Symplectic Structure
• Unnamed Item

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