Probabilistic Mechanism of the Superiority of Stochastic Symplectic Methods via Large Deviations Principles
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：陈楚楚（中科院）
：2022-05-18 10:00
：腾讯会议ID：978449682（无密码）
报告人：陈楚楚（中科院）
时 间：5月18日上午10:00
地 点：腾讯会议ID：978449682（无密码）
内容摘要:
A large number of numerical experiments show that stochastic symplectic methods possess excellent long-time computational stability, when applied to stochastic Hamiltonian systems. In this talk, we attempt to give an explanation on the superiority of stochastic symplectic methods by means of large deviations principle. We prove that stochastic symplectic methods are able to asymptotically preserve the large deviations principles for some important observables of the exact solution, while non-symplectic ones do not. This indicates that stochastic symplectic methods are able to preserve the asymptotic results on rare event probabilities of the original system, and may provide an effective approach to approximating rate functions of large deviations principles.
个人简介：
陈楚楚，中国科学院数学与系统科学研究院，副研究员。2015年在数学与系统科学研究院获博士学位，2015-2017年先后在普渡大学和密歇根州立大学从事博士后研究工作。主要研究方向为随机偏微分方程保结构算法及其理论分析。
联系人：陈黄鑫
