Open AccessLarge deviation principle for stochastic Burgers type equation with reflection
Author(s) -
Ran Wang,
Jianliang Zhai,
Shiling Zhang
Publication year - 2021
Publication title -
communications on pure andamp applied analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.077
H-Index - 42
eISSN - 1553-5258
pISSN - 1534-0392
DOI - 10.3934/cpaa.2021175
Subject(s) - burgers' equation , reflection (computer programming) , type (biology) , mathematics , mathematical analysis , statistical physics , physics , computer science , geology , partial differential equation , programming language , paleontology
In this paper, we establish a large deviation principle for stochastic Burgers type equation with reflection perturbed by the small multiplicative noise. The main difficulties come from the highly non-linear coefficient and the singularity caused by the reflection. Here, we adopt a new sufficient condition for the weak convergence criteria, which is proposed by Matoussi, Sabbagh and Zhang [ 14 ].