随机正压可压缩欧拉方程的收敛有限体积格式。

IF 0.6 4区 数学 Q4 STATISTICS & PROBABILITY Esaim-Probability and Statistics Pub Date : 2023-10-23 DOI:10.1051/m2an/2023085
Abhishek Chaudhary, Ujjwal Koley
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引用次数: 1

摘要

本文分析了由乘性布朗噪声驱动的三维正压可压缩欧拉方程的半离散有限体积格式。我们导出了数值近似的必要先验估计,并证明了由数值近似产生的Young测度收敛于随机可压缩欧拉系统的耗散测度值鞅解。这些解决方案在概率上是弱的,因为驱动噪声和相关过滤是解决方案的组成部分。此外,由于底层系统的弱(测量值)-强唯一性原理,我们证明了极限系统的正则解的数值解至少在后者的寿命上是强收敛的。据我们所知,这是第一次尝试证明底层系统的数值近似的收敛性。
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A convergent finite volume scheme for the stochastic barotropic compressible Euler equations.
In this paper, we analyze a semi-discrete finite volume scheme for the three dimensional barotropic compressible Euler equations driven by a multiplicative Brownian noise. We derive necessary a priori estimates for numerical approximations, and show that the Young measure generated by the numerical approximations converge to a dissipative measure--valued martingale solution to the stochastic compressible Euler system. These solutions are probabilistically weak in the sense that the driving noise and associated filtration are integral part of the solution. Moreover, we demonstrate strong convergence of numerical solutions to the regular solution of the limit systems at least on the lifespan of the latter, thanks to the weak (measure-valued)--strong uniqueness principle for the underlying system. To the best of our knowledge, this is the first attempt to prove the convergence of numerical approximations for the underlying system.
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来源期刊
Esaim-Probability and Statistics
Esaim-Probability and Statistics STATISTICS & PROBABILITY-
CiteScore
1.00
自引率
0.00%
发文量
14
审稿时长
>12 weeks
期刊介绍: The journal publishes original research and survey papers in the area of Probability and Statistics. It covers theoretical and practical aspects, in any field of these domains. Of particular interest are methodological developments with application in other scientific areas, for example Biology and Genetics, Information Theory, Finance, Bioinformatics, Random structures and Random graphs, Econometrics, Physics. Long papers are very welcome. Indeed, we intend to develop the journal in the direction of applications and to open it to various fields where random mathematical modelling is important. In particular we will call (survey) papers in these areas, in order to make the random community aware of important problems of both theoretical and practical interest. We all know that many recent fascinating developments in Probability and Statistics are coming from "the outside" and we think that ESAIM: P&S should be a good entry point for such exchanges. Of course this does not mean that the journal will be only devoted to practical aspects.
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