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Global well-posedness and large deviations for 3D stochastic Burgers equations

Rangrang Zhang, Guoli Zhou, Boling Guo, Jiang-lun Wu Orcid Logo

Zeitschrift für angewandte Mathematik und Physik, Volume: 71, Issue: 1

Swansea University Author: Jiang-lun Wu Orcid Logo

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DOI (Published version): 10.1007/s00033-020-1259-z

Abstract

In this paper, we study the stochastic vector-valued Burgers equations with non − periodic boundary conditions. We first apply a contraction principle argument to show local existence and uniqueness of a mild solution to this model. Then, towards obtaining the global well-posedness, we derive a prio...

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Published in: Zeitschrift für angewandte Mathematik und Physik
ISSN: 0044-2275 1420-9039
Published: Springer Science and Business Media LLC 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa53378
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first_indexed 2020-01-28T19:39:05Z
last_indexed 2020-09-17T03:16:36Z
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spelling 2020-01-28T14:34:47.0705505 v2 53378 2020-01-28 Global well-posedness and large deviations for 3D stochastic Burgers equations dbd67e30d59b0f32592b15b5705af885 0000-0003-4568-7013 Jiang-lun Wu Jiang-lun Wu true false 2020-01-28 SMA In this paper, we study the stochastic vector-valued Burgers equations with non − periodic boundary conditions. We first apply a contraction principle argument to show local existence and uniqueness of a mild solution to this model. Then, towards obtaining the global well-posedness, we derive a priori estimates of the local solution by utilising the maximum principle. Finally, we establish, by means of the weak convergence approach, the Freidlin-Wentzell type large deviation principle for 3D stochastic Burgers equations when the noise term goes to zero. Journal Article Zeitschrift für angewandte Mathematik und Physik 71 1 Springer Science and Business Media LLC 0044-2275 1420-9039 3D stochastic Burgers equations; global well-posedness; the Freidlin-Wentzell type large deviation principle. 1 2 2020 2020-02-01 10.1007/s00033-020-1259-z COLLEGE NANME Mathematics COLLEGE CODE SMA Swansea University 2020-01-28T14:34:47.0705505 2020-01-28T14:34:47.0705505 Rangrang Zhang 1 Guoli Zhou 2 Boling Guo 3 Jiang-lun Wu 0000-0003-4568-7013 4 53378__16458__db20341c3f4641c89d185350ac83a455.pdf ZhangZhouGuoWu.pdf 2020-01-28T14:38:36.5727155 Output 195271 application/pdf Accepted Manuscript true 2021-01-29T00:00:00.0000000 true eng
title Global well-posedness and large deviations for 3D stochastic Burgers equations
spellingShingle Global well-posedness and large deviations for 3D stochastic Burgers equations
Jiang-lun Wu
title_short Global well-posedness and large deviations for 3D stochastic Burgers equations
title_full Global well-posedness and large deviations for 3D stochastic Burgers equations
title_fullStr Global well-posedness and large deviations for 3D stochastic Burgers equations
title_full_unstemmed Global well-posedness and large deviations for 3D stochastic Burgers equations
title_sort Global well-posedness and large deviations for 3D stochastic Burgers equations
author_id_str_mv dbd67e30d59b0f32592b15b5705af885
author_id_fullname_str_mv dbd67e30d59b0f32592b15b5705af885_***_Jiang-lun Wu
author Jiang-lun Wu
author2 Rangrang Zhang
Guoli Zhou
Boling Guo
Jiang-lun Wu
format Journal article
container_title Zeitschrift für angewandte Mathematik und Physik
container_volume 71
container_issue 1
publishDate 2020
institution Swansea University
issn 0044-2275
1420-9039
doi_str_mv 10.1007/s00033-020-1259-z
publisher Springer Science and Business Media LLC
document_store_str 1
active_str 0
description In this paper, we study the stochastic vector-valued Burgers equations with non − periodic boundary conditions. We first apply a contraction principle argument to show local existence and uniqueness of a mild solution to this model. Then, towards obtaining the global well-posedness, we derive a priori estimates of the local solution by utilising the maximum principle. Finally, we establish, by means of the weak convergence approach, the Freidlin-Wentzell type large deviation principle for 3D stochastic Burgers equations when the noise term goes to zero.
published_date 2020-02-01T04:06:18Z
_version_ 1763753462981459968
score 11.013148

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