Large Deviation Principles of Invariant Measures of Stochastic Reaction-Diffusion Lattice Systems

活动时间:2024-05-19 09:00




王碧祥,美国新墨西哥矿业理工大学数学系终身教授,博士生导师,于兰州大学获理学学士、硕士与博士学位。曾在北京应用物理与计算数学研究所和美国杨百翰大学从事博士后研究工作, 在美国堪萨斯大学担任访问助理教授。

王碧祥教授主要从事确定与随机动力系统和非线性偏微分方程理论与应用等领域的研究,是国际无穷维动力系统代表性学者之一。目前已发表SCI 论文150 余篇,研究主要成果发表于《Mathematische Annalen》,《Transactions of the American Mathematical Society》,《Journal of Functional Analysis》,《SIAM Journal on Applied Dynamical Systems》《Proceedings of the American Mathematical Society》,《Journal of Differential Equations》,《Science China Mathematics》,《Stochastic Processes and their Applications 》,《Nonlinearity》,《Physica D: Nonlinear Phenomena》,《Journal of Dynamics and Differential Equations》等多个国际知名数学学术期刊上,研究成果已被国际同行引用6000余次(谷歌学术),并多次获美国国家科学基金资助。


In this talk, we discuss the large deviation principle of invariant measures of stochastic reaction-diffusion lattice systems driven by multiplicative noise. We first show that any limit of a sequence of invariant measures of the stochastic system must be an invariant measure of the deterministic limiting system as noise intensity approaches zero. We then prove the uniform Freidlin-Wentzell large deviations of solution paths over all initial data and the uniform DemboZeitouni large deviations of solution paths over a compact set of initial data. We finally establish the large deviations of invariant measures by combining the idea of tail-ends estimates and the argument of weighed spaces.