论文标题
学习相关随机电力系统动力学的演变
Learning the Evolution of Correlated Stochastic Power System Dynamics
论文作者
论文摘要
提出了一种机器学习技术，以量化具有空间相关随机强迫的电力系统动力学的不确定性。我们学习了一维线性偏微分方程，以实现实现量的概率密度函数。该方法适用于高维系统，有助于减轻维度的诅咒。
A machine learning technique is proposed for quantifying uncertainty in power system dynamics with spatiotemporally correlated stochastic forcing. We learn one-dimensional linear partial differential equations for the probability density functions of real-valued quantities of interest. The method is suitable for high-dimensional systems and helps to alleviate the curse of dimensionality.
