High-Order Symplectic Schemes for Stochastic Hamiltonian Systems
高阶辛格式随机Hamilton系统作者机构: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.