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Status已发表Published
TitleLarge deviation principles for first-order scalar conservation laws with stochastic forcing
Creator
Date Issued2020-02-01
Source PublicationAnnals of Applied Probability
ISSN1050-5164
Volume30Issue:1Pages:324-367
Abstract

In this paper, we established the Freidlin-Wentzell-type large deviation principles for first-order scalar conservation laws perturbed by small multiplicative noise. Due to the lack of the viscous terms in the stochastic equations, the kinetic solution to the Cauchy problem for these first-order conservation laws is studied. Then, based on the well-posedness of the kinetic solutions, we show that the large deviations holds by utilising the weak convergence approach.

KeywordFirst-order conservation laws Kinetic solution Large deviations Weak convergence approach
DOI10.1214/19-AAP1503
URLView source
Indexed BySCIE
Language英语English
WOS Research AreaMathematics
WOS SubjectStatistics & Probability
WOS IDWOS:000517756200009
Scopus ID2-s2.0-85087551861
Citation statistics
Cited Times:26[WOS]   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
Identifierhttp://repository.uic.edu.cn/handle/39GCC9TT/10494
CollectionResearch outside affiliated institution
Affiliation
1.RCSDS,Academy of Mathematics and Systems Science,Chinese Academy of Sciences,China
2.Department of Mathematics,Swansea University,United Kingdom
3.School of Mathematics and Statistics,Beijing Institute of Technology,China
4.School of Mathematics,University of Manchester,United Kingdom
Recommended Citation
GB/T 7714
Dong, Zhao,Wu, Jianglun,Zhang, Rangranget al. Large deviation principles for first-order scalar conservation laws with stochastic forcing[J]. Annals of Applied Probability, 2020, 30(1): 324-367.
APA Dong, Zhao, Wu, Jianglun, Zhang, Rangrang, & Zhang, Tusheng. (2020). Large deviation principles for first-order scalar conservation laws with stochastic forcing. Annals of Applied Probability, 30(1), 324-367.
MLA Dong, Zhao,et al."Large deviation principles for first-order scalar conservation laws with stochastic forcing". Annals of Applied Probability 30.1(2020): 324-367.
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