Global Well-Posedness of Stochastic 3D Primitive Equations with Anticipating Initial Data
Global Well-Posedness of Stochastic 3D Primitive Equations with Anticipating Initial Data
复制标题
DOI：
10.1007/s10884-022-10211-9
复制
发表时间：
2022-10
影响因子：
1.3
通讯作者：
Z. Dong;B. Guo;Lidan Wang;Guoli Zhou
中科院分区：
文献类型：
--
作者：
Z. Dong;B. Guo;Lidan Wang;Guoli Zhou
本文研究了具有随机初始条件的三维随机原始方程在仿射线性乘性白色噪声驱动下的解。我们的主要目标是在初始条件的充分Malliavin正则性下得到随机方程的整体适定性。除了传统的策略，我们采用动力系统的方法和技术从Malliavin演算攻击的全局适定性问题的PE。
In this article, we consider 3D stochastic primitive equations (PEs) driven by affine-linear multiplicative white noise, with random initial condition. Our main objective is to obtain the global well-posedness of the stochastic equations under the sufficient Malliavin regularity of the initial condition. Apart from the conventional strategy, we adopt the dynamical system approach and techniques from Malliavin calculus to attack the global well-posedness problem for the PEs.