High-Order Symplectic Schemes for Stochastic Hamiltonian Systems

Content Provider	Semantic Scholar
Author	Deng, Jian Anton, Cristina Wong, Yau Shu
Copyright Year	2014
Abstract	The construction of symplectic numerical schemes for stochastic Hamiltonian systems is studied. An approach based on generating functions method is proposed to generate the stochastic symplectic integration of any desired order. In general the proposed symplectic schemes are fully implicit, and they become computationally expensive for mean square orders greater than two. However, for stochastic Hamiltonian systems preserving Hamiltonian functions, the high-order symplectic methods have simpler forms than the explicit Taylor expansion schemes. A theoretical analysis of the convergence and numerical simulations are reported for several symplectic integrators. The numerical case studies confirm that the symplectic methods are efficient computational tools for long-term simulations. AMS subject classifications: 60H10, 65C30, 65P10
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://global-sci.com/issue/v16/n1/pdf/169.pdf
Language	English
Access Restriction	Open
Subject Keyword	Analysis of algorithms Approximation Classification Convergence (action) Hamiltonian (quantum mechanics) Mean squared error Numerical analysis Numerical method Simulation Symplectic integrator VHDL-AMS orders - HL7PublishingDomain
Content Type	Text
Resource Type	Article