Large deviation principle for stochastic Burgers type equation with reflection | Zendy

Ran Wang | Zendy; Jianliang Zhai | Zendy; Shiling Zhang | Zendy

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Large 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 ].

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