Large deviation principle of occupation measures for non-linear monotone SPDEs

作     者：Ran Wang Jie Xiong Lihu Xu Ran Wang;Jie Xiong;Lihu Xu

作者机构：School of Mathematics and StatisticsWuhan UniversityWuhan 430072China Department of Mathematics and International Center for MathematicsSouthern University of Science and TechnologyShenzhen 518055China Department of MathematicsFaculty of Science and TechnologyUniversity of MacaoMacaoChina 

出 版 物：《Science China Mathematics》 (中国科学：数学（英文版）)

年 卷 期：2021年第64卷第4期

页      面：799-822页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 0701[理学-数学] 

基　　金：supported by National Natural Science Foundation of China(Grant Nos.11431014 and 11671076) supported by University of Macao Multi-Year Research Grant(Grant No.MYRG2016-00025-FST) Science and Technology Development Fund,Macao SAR(Grant Nos.025/2016/A1,030/2016/A1 and 038/2017/A1)the Faculty of Science and Technology,University of Macao,for financial support and hospitality。 

主　　题：stochastic partial differential equation large deviation principle occupation measure hyperexponential recurrence 

摘      要：Using the hyper-exponential recurrence criterion,we establish the occupation measures’large deviation principle for a class of non-linear monotone stochastic partial differential equations(SPDEs)driven by Wiener noise,including the stochastic p-Laplace equation,the stochastic porous medium equation and the stochastic fast-diffusion equation.We also propose a framework for verifying hyper-exponential recurrence,and apply it to study the large deviation problems for strong dissipative SPDEs.These SPDEs can be stochastic systems driven by heavy-tailedα-stable process.