| bibtype |
J -
Journal Article
|
| ARLID |
0561775 |
| utime |
20230418205119.2 |
| mtime |
20221003235959.9 |
| SCOPUS |
85113387774 |
| WOS |
000688431300002 |
| DOI |
10.1007/s10959-021-01125-1 |
| title
(primary) (eng) |
Large and Moderate Deviations Principles and Central Limit Theorem for the Stochastic 3D Primitive Equations with Gradient-Dependent Noise |
| specification |
| page_count |
46 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0254080 |
| ISSN |
0894-9840 |
| title
|
Journal of Theoretical Probability |
| volume_id |
35 |
| volume |
3 (2022) |
| page_num |
1736-1781 |
| publisher |
|
|
| keyword |
large deviations principle |
| keyword |
moderate deviations principle |
| keyword |
primitive equations |
| keyword |
weak convergence approach |
| author
(primary) |
| ARLID |
cav_un_auth*0370372 |
| name1 |
Slavík |
| name2 |
Jakub |
| institution |
UTIA-B |
| full_dept (cz) |
Stochastická informatika |
| full_dept (eng) |
Department of Stochastic Informatics |
| department (cz) |
SI |
| department (eng) |
SI |
| full_dept |
Department of Stochastic Informatics |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| source |
|
| cas_special |
| abstract
(eng) |
We establish the large deviations principle (LDP) and the moderate deviations principle (MDP) and an almost sure version of the central limit theorem (CLT) for the stochastic 3D viscous primitive equations driven by a multiplicative white noise allowing dependence on spatial gradient of solutions with initial data in H2. The LDP is established using the weak convergence approach of Budjihara and Dupuis and uniform version of the stochastic Gronwall lemma. The result corrects a minor technical issue in Z. Dong, J. Zhai, and R. Zhang: Large deviations principles for 3D stochastic primitive equations, J. Differential Equations, 263(5):3110–3146, 2017, and establishes the result for a more general noise. The MDP is established using a similar argument. |
| result_subspec |
WOS |
| RIV |
BA |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2023 |
| num_of_auth |
1 |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0335180 |
| confidential |
S |
| mrcbC91 |
C |
| mrcbT16-e |
STATISTICSPROBABILITY |
| mrcbT16-j |
0.625 |
| mrcbT16-s |
0.659 |
| mrcbT16-D |
Q3 |
| mrcbT16-E |
Q2 |
| arlyear |
2022 |
| mrcbU14 |
85113387774 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000688431300002 WOS |
| mrcbU63 |
cav_un_epca*0254080 Journal of Theoretical Probability 0894-9840 1572-9230 Roč. 35 č. 3 2022 1736 1781 Springer |
|