具有分布交互核的多维稳定驱动麦金-弗拉索夫自变量:噪声正则化视角

IF 1.4 3区 数学 Q2 MATHEMATICS, APPLIED
P. -E. Chaudru de Raynal, J. -F. Jabir, S. Menozzi
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引用次数: 0

摘要

在本研究中,我们致力于在 Lebesgue-Besov 空间框架内,建立具有加性稳定噪声和卷积型非线性漂移与奇异相互作用核的 McKean-Vlasov SDE 的弱和强好求性。我们证明,该系统的良好拟合性在噪声缩放所产生的阈值(以正则性指数表示)上成立,而且相应的 SDE 可以在经典意义上理解。特别是,我们从数量上描述了非线性如何允许超越更强的阈值,这些阈值来自 Bony 的旁积规则,通常针对具有奇异相互作用核的线性 SDE。我们还具体描述了驱动噪声的稳定指数和漂移参数在弱唯一性和强唯一性之间发生二分时的函数特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Multidimensional stable driven McKean–Vlasov SDEs with distributional interaction kernel: a regularization by noise perspective

Multidimensional stable driven McKean–Vlasov SDEs with distributional interaction kernel: a regularization by noise perspective

In this work, we are interested in establishing weak and strong well-posedness for McKean–Vlasov SDEs with additive stable noise and a convolution type non-linear drift with singular interaction kernel in the framework of Lebesgue–Besov spaces. We prove that the well-posedness of the system holds for the thresholds (in terms of regularity indexes) deriving from the scaling of the noise and that the corresponding SDE can be understood in the classical sense. Especially, we characterize quantitatively how the non-linearity allows to go beyond the stronger thresholds, coming from Bony’s paraproduct rule, usually obtained for linear SDEs with singular interaction kernels. We also specifically characterize in function of the stability index of the driving noise and the parameters of the drift when the dichotomy between weak and strong uniqueness occurs.

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来源期刊
CiteScore
2.70
自引率
13.30%
发文量
54
期刊介绍: Stochastics and Partial Differential Equations: Analysis and Computations publishes the highest quality articles presenting significantly new and important developments in the SPDE theory and applications. SPDE is an active interdisciplinary area at the crossroads of stochastic anaylsis, partial differential equations and scientific computing. Statistical physics, fluid dynamics, financial modeling, nonlinear filtering, super-processes, continuum physics and, recently, uncertainty quantification are important contributors to and major users of the theory and practice of SPDEs. The journal is promoting synergetic activities between the SPDE theory, applications, and related large scale computations. The journal also welcomes high quality articles in fields strongly connected to SPDE such as stochastic differential equations in infinite-dimensional state spaces or probabilistic approaches to solving deterministic PDEs.
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