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Learning the Evolution of Correlated Stochastic Power System Dynamics (2207.13310v1)
Published 27 Jul 2022 in cs.LG, math.DS, math.PR, physics.data-an, and stat.AP
Abstract: 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.
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