Accurate Stationary Densities with Partitioned Numerical Methods for Stochastic Differential Equations

DOI10.1137/060677148zbMath1197.60068OpenAlexW1978330428WikidataQ56838957 ScholiaQ56838957MaRDI QIDQ3578046

Grant Lythe, Kevin Burrage

Publication date: 13 July 2010

Published in: SIAM Journal on Numerical Analysis (Search for Journal in Brave)

Full work available at URL: https://espace.library.uq.edu.au/view/UQ:183633/UQ183633_OA.pdf

zbMATH Keywords

stationary distributionstochastic Runge-Kutta methodsleapfrog methodsdamped harmonic oscillators with noise

Mathematics Subject Classification ID

Computational methods for stochastic equations (aspects of stochastic analysis) (60H35) Numerical solutions to stochastic differential and integral equations (65C30)

Related Items (13)

General order conditions for stochastic partitioned Runge-Kutta methods ⋮ Numerical preservation of long-term dynamics by stochastic two-step methods ⋮ Fractional Euler Limits and their Applications ⋮ Improved \(\vartheta\)-methods for stochastic Volterra integral equations ⋮ On the numerical structure preservation of nonlinear damped stochastic oscillators ⋮ Structure-preserving Runge-Kutta methods for stochastic Hamiltonian equations with additive noise ⋮ Accurate stationary densities with partitioned numerical methods for stochastic partial differential equations ⋮ Two-step Runge-Kutta methods for stochastic differential equations ⋮ Locally linearized methods for the simulation of stochastic oscillators driven by random forces ⋮ Filon quadrature for stochastic oscillators driven by time-varying forces ⋮ Long-term analysis of stochastic \(\theta\)-methods for damped stochastic oscillators ⋮ Stability issues for selected stochastic evolutionary problems: a review ⋮ A new class of structure-preserving stochastic exponential Runge-Kutta integrators for stochastic differential equations

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