2013
DOI: 10.1155/2013/653160
|View full text |Cite
|
Sign up to set email alerts
|

Random Dynamics of the Stochastic Boussinesq Equations Driven by Lévy Noises

Abstract: This paper is devoted to the investigation of random dynamics of the stochastic Boussinesq equations driven by Lévy noise. Some fundamental properties of a subordinator Lévy process and the stochastic integral with respect to a Lévy process are discussed, and then the existence, uniqueness, regularity, and the random dynamical system generated by the stochastic Boussinesq equations are established. Finally, some discussions on the global weak solution of the stochastic Boussinesq equations driven by general Lé… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
3
0

Year Published

2016
2016
2020
2020

Publication Types

Select...
3
2

Relationship

0
5

Authors

Journals

citations
Cited by 5 publications
(3 citation statements)
references
References 17 publications
0
3
0
Order By: Relevance
“…Lemma 3.7 (Corollary 8.1, [14]). Assume that: p ∈ (1, 2]; X is a subordinator Lévy process from the class Sub(p); E is a separable type p Banach space; U is a separable Hilbert space; E ⊂ U ; and W = (W (t), t ≥ 0) is an U -valued Wiener process.…”
Section: Stochastic Convolution Of β-Stable Noisementioning
confidence: 98%
“…Lemma 3.7 (Corollary 8.1, [14]). Assume that: p ∈ (1, 2]; X is a subordinator Lévy process from the class Sub(p); E is a separable type p Banach space; U is a separable Hilbert space; E ⊂ U ; and W = (W (t), t ≥ 0) is an U -valued Wiener process.…”
Section: Stochastic Convolution Of β-Stable Noisementioning
confidence: 98%
“…ω ∈ D(R, X),z(ϑ s ω)(t) =ẑ(ω)(t + s), t, s ∈ R.Proof. The proofs of the first three parts follows from closely from Theorem 4.8 and Proposition 8.4 in[9], see also Theorem 9 in[23]. For the last part, since (ϑ s ω)(r) = ω(r + s) − ω(s), r ∈ R, we havê…”
mentioning
confidence: 94%
“…The existence of pull-back random attractors has been extensively studied by many authors for several kinds of SPDEs defined on bounded domain (see, e.g. [16][17][18]). For the unbounded domain case, the situation becomes much more complicated, and we need to deal with more difficulties caused by the unboundedness of the spacial domain.…”
mentioning
confidence: 99%