scientific article; zbMATH DE number 7485163
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Publication:5862207
zbMath1499.65009MaRDI QIDQ5862207
Yau Shu Wong, Cristina Anton, Jian Deng
Publication date: 7 March 2022
Full work available at URL: https://www.global-sci.org/intro/article_detail/ijnam/536.html
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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)
Related Items (8)
Splitting integrators for stochastic Lie–Poisson systems ⋮ Explicit pseudo-symplectic Runge-Kutta methods for stochastic Hamiltonian systems ⋮ Analysis of a splitting scheme for a class of nonlinear stochastic Schrödinger equations ⋮ High Order Conformal Symplectic and Ergodic Schemes for the Stochastic Langevin Equation via Generating Functions ⋮ High order numerical integrators for single integrand Stratonovich SDEs ⋮ Explicit pseudo-symplectic methods based on generating functions for stochastic Hamiltonian systems ⋮ Structure-Preserving Numerical Methods for Stochastic Poisson Systems ⋮ Projection methods for stochastic differential equations with conserved quantities
Cites Work
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- Stochastic Hamiltonian dynamical systems
- Higher-order implicit strong numerical schemes for stochastic differential equations
- Weak symplectic schemes for stochastic Hamiltonian equations
- Symplectic Numerical Schemes for Stochastic Systems Preserving Hamiltonian Functions
- Quasi-symplectic methods for Langevin-type equations
- Symplectic Integration of Hamiltonian Systems with Additive Noise
- Numerical Methods for Stochastic Systems Preserving Symplectic Structure
- High-Order Symplectic Schemes for Stochastic Hamiltonian Systems
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