论文标题
随机模型减少：收敛和应用到气候方程
Stochastic model reduction: convergence and applications to climate equations
论文作者
论文摘要
我们研究了无限尺寸希尔伯特空间中进化方程的随机模型，并通过Wong-Zakai类型的抽象结果显示了与缩放的Ornstein-Uhlenbeck过程驱动的随机方程的抽象结果。根据减少变量和驱动噪声之间存在二次相互作用的情况，研究了弱收敛和强。最后，我们能够将结果应用于气候建模中使用的一类方程。
We study stochastic model reduction for evolution equations in infinite dimensional Hilbert spaces, and show the convergence to the reduced equations via abstract results of Wong-Zakai type for stochastic equations driven by a scaled Ornstein-Uhlenbeck process. Both weak and strong convergence are investigated, depending on the presence of quadratic interactions between reduced variables and driving noise. Finally, we are able to apply our results to a class of equations used in climate modeling.
