High-Order Symplectic Schemes for Stochastic Hamiltonian Systems

作     者：Jian Deng Cristina Anton Yau Shu Wong 

作者机构：Department of Mathematical and Statistical SciencesUniversity of AlbertaEdmontonAB T6G 2G1Canada. Department of Mathematics and StatisticsGrant MacEwan UniversityEdmontonAB T5J 4S2Canada. 

出 版 物：《Communications in Computational Physics》 (计算物理通讯（英文）)

年 卷 期：2014年第16卷第6期

页      面：169-200页

核心收录：

学科分类：07[理学] 0704[理学-天文学] 0701[理学-数学] 0702[理学-物理学] 070101[理学-基础数学] 

基　　金：Natural Sciences and Engineering Research Council of Canada 

主　　题：Stochastic Hamiltonian systems symplectic integration mean-square convergence high-order schemes. 

摘      要：The construction of symplectic numerical schemes for stochastic Hamiltonian systems is *** approach based on generating functions method is proposed to generate the stochastic symplectic integration of any desired *** general the proposed symplectic schemes are fully implicit,and they become computationally expensive for mean square orders greater than ***,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 *** numerical case studies confirm that the symplectic methods are efficient computational tools for long-term simulations.