STOCHASTIC PARTITIONED AVERAGED VECTOR FIELD METHODS FOR STOCHASTIC DIFFERENTIAL EQUATIONS WITH A CONSERVED QUANTITY
DOI10.11948/20180254zbMath1459.60127OpenAlexW2980261818MaRDI QIDQ5859442
Qiang Ma, Xiuyan Li, Xiao-Hua Ding
Publication date: 16 April 2021
Published in: Journal of Applied Analysis & Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.11948/20180254
convergence analysisstochastic differential equationsconserved quantitystochastic partitioned averaged vector field methods
Probabilistic models, generic numerical methods in probability and statistics (65C20) Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Numerical solutions to stochastic differential and integral equations (65C30)
Cites Work
- Unnamed Item
- Unnamed Item
- High strong order stochastic Runge-Kutta methods for Stratonovich stochastic differential equations with scalar noise
- Stochastic symplectic partitioned Runge-Kutta methods for stochastic Hamiltonian systems with multiplicative noise
- Exponential mean square stability of numerical methods for systems of stochastic differential equations
- Preservation of quadratic invariants of stochastic differential equations via Runge-Kutta methods
- Energy-preserving integrators for stochastic Poisson systems
- Convergence and stability of the split-step \(\theta \)-method for stochastic differential equations
- Partitioned averaged vector field methods
- A family of fully implicit Milstein methods for stiff stochastic differential equations with multiplicative noise
- Energy conservative stochastic difference scheme for stochastic Hamilton dynamical systems
- Numerical methods for strong solutions of stochastic differential equations: an overview
- The improved split-step backward Euler method for stochastic differential delay equations
- Discrete Gradient Approach to Stochastic Differential Equations with a Conserved Quantity
- Geometric integration using discrete gradients
- Discrete gradient methods for solving ODEs numerically while preserving a first integral
- Energy- and Quadratic Invariants--Preserving Integrators Based upon Gauss Collocation Formulae
- Discrete gradient methods and linear projection methods for preserving a conserved quantity of stochastic differential equations
- Energy-preserving numerical methods for Landau–Lifshitz equation
- A Sixth Order Averaged Vector Field Method
- A new class of energy-preserving numerical integration methods
- Geometric Numerical Integration
- High strong order explicit Runge-Kutta methods for stochastic ordinary differential equations
This page was built for publication: STOCHASTIC PARTITIONED AVERAGED VECTOR FIELD METHODS FOR STOCHASTIC DIFFERENTIAL EQUATIONS WITH A CONSERVED QUANTITY