Projection methods for stochastic differential equations with conserved quantities

DOI10.1007/s10543-016-0614-0zbMath1354.60081arXiv1601.04157OpenAlexW2296570413MaRDI QIDQ727906

Songhe Song, Liying Zhang, Jialin Hong, Weien Zhou

Publication date: 21 December 2016

Published in: BIT (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1601.04157

zbMATH Keywords

stochastic differential equationsprojection methodsconserved quantitiesmean-square convergence

Mathematics Subject Classification ID

Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Computational methods for stochastic equations (aspects of stochastic analysis) (60H35) Numerical solutions to stochastic differential and integral equations (65C30) Numerical integration (65D30)

Related Items (13)

Numerical simulations for stochastic differential equations on manifolds by stochastic symmetric projection method ⋮ Numerical Simulations of Stochastic Differential Equations with Multiple Conserved Quantities by Conservative Methods ⋮ Arbitrary high-order EQUIP methods for stochastic canonical Hamiltonian systems ⋮ Stochastic dynamics for inextensible fibers in a spatially semi-discrete setting ⋮ Numerical investigation of stochastic canonical Hamiltonian systems by high order stochastic partitioned Runge-Kutta methods ⋮ Modified averaged vector field methods preserving multiple invariants for conservative stochastic differential equations ⋮ Long-term adaptive symplectic numerical integration of linear stochastic oscillators driven by additive white noise ⋮ Drift-preserving numerical integrators for stochastic Hamiltonian systems ⋮ A stochastic Gronwall inequality and applications to moments, strong completeness, strong local Lipschitz continuity, and perturbations ⋮ An energy-momentum conserving scheme for Hamiltonian wave equation based on multiquadric trigonometric quasi-interpolation ⋮ Stochastic Global Momentum-Preserving Schemes for Two-Dimensional Stochastic Partial Differential Equations ⋮ Data-driven approximation of the Koopman generator: model reduction, system identification, and control ⋮ Discrete gradient methods and linear projection methods for preserving a conserved quantity of stochastic differential equations

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