Stochastic symplectic partitioned Runge-Kutta methods for stochastic Hamiltonian systems with multiplicative noise

DOI10.1016/j.amc.2014.12.045zbMath1338.65012OpenAlexW2045177440MaRDI QIDQ298766

Qiang Ma, Xiao-Hua Ding

Publication date: 21 June 2016

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2014.12.045

zbMATH Keywords

stochastic differential equationssymplectic integratorsstochastic generating functionsstochastic Hamiltonian systemsstochastic partitioned Runge-Kutta methods

Mathematics Subject Classification ID

Numerical solutions to stochastic differential and integral equations (65C30) Discretization methods and integrators (symplectic, variational, geometric, etc.) for dynamical systems (37M15)

Related Items (14)

Stochastic multi-symplectic Runge-Kutta methods for stochastic Hamiltonian PDEs ⋮ Stochastic discrete Hamiltonian variational integrators ⋮ General order conditions for stochastic partitioned Runge-Kutta methods ⋮ Structure-preserving stochastic Runge-Kutta-Nyström methods for nonlinear second-order stochastic differential equations with multiplicative noise ⋮ Arbitrary high-order EQUIP methods for stochastic canonical Hamiltonian systems ⋮ Data-driven structure-preserving model reduction for stochastic Hamiltonian systems ⋮ Numerical investigation of stochastic canonical Hamiltonian systems by high order stochastic partitioned Runge-Kutta methods ⋮ Symplectic waveform relaxation methods for Hamiltonian systems ⋮ Long-term adaptive symplectic numerical integration of linear stochastic oscillators driven by additive white noise ⋮ High-order stochastic symplectic partitioned Runge-Kutta methods for stochastic Hamiltonian systems with additive noise ⋮ Discrete stochastic port-Hamiltonian systems ⋮ Issues in the Software Implementation of Stochastic Numerical Runge–Kutta ⋮ STOCHASTIC PARTITIONED AVERAGED VECTOR FIELD METHODS FOR STOCHASTIC DIFFERENTIAL EQUATIONS WITH A CONSERVED QUANTITY ⋮ Discrete gradient methods and linear projection methods for preserving a conserved quantity of stochastic differential equations

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