2015
DOI: 10.1016/j.na.2015.08.009
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Existence and upper semicontinuity of bi-spatial pullback attractors for smoothing cocycles

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Cited by 29 publications
(37 citation statements)
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“…Slightly modifying the proof one can establish the previous lemma also for m-NDS not upper semi-continuous but with closed graph, see [6] and also [7].…”
Section: Existence Of a Backwards Bounded Pullback Attractormentioning
confidence: 99%
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“…Slightly modifying the proof one can establish the previous lemma also for m-NDS not upper semi-continuous but with closed graph, see [6] and also [7].…”
Section: Existence Of a Backwards Bounded Pullback Attractormentioning
confidence: 99%
“…The first is the existence of the (H, V )-pullback attractor A = {A(τ )} τ ∈R for (1.1), which is a non-autonomous set pullback attracting bounded sets of H under the topology of V , where H := (L 2 (O)) d , V := (H 1 0 (O)) d . This is a study of bi-spatial attractors which attracted much attention these years due to their higher regularity and stronger attracting ability compared with usual attractor, see [7,13,12] for single-valued non-autonomous/random cocycles and [21,19] for multi-valued semi-groups and random cocycles, respectively. In this paper, we develop a study for m-NDS which can be regarded as an extension of the bi-spacial attractor theory on one hand, and is interesting because of the multi-valued feature on the other hand.…”
Section: Introductionmentioning
confidence: 99%
“…A pullback attractor given in Def.3.1 involves two phases spaces, in this respect, the concept coincides with bi-spatial attractors (see e.g. [7,14,15,16,18,28,29]). However, a bi-spatial attractor is sufficiently regular, which means it is compact and attracting in the terminate space E (rather than E 0 only).…”
Section: Remarkmentioning
confidence: 96%
“…The wave equation without the dispersive term was also discussed in Wang [21] and Yang, Duan and Kloeden [24] for such additive noise and in Wang, Zhou and Gu [22] for usual multiplicative noise, i.e. Su = u, also see [8,9,11,13,17,18,20,25,26,32].…”
Section: Introductionmentioning
confidence: 99%