# Learning the Evolution of Correlated Stochastic Power System Dynamics

arXiv: https://arxiv.org/abs/2207.13310 · HTML: https://mlsanity.com/paper/2207.13310/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 27 Jul 2022.

## Builds on (most cited first, 2 of 2)

- [Nonintrusive Uncertainty Quantification of Dynamic Power Systems Subject to Stochastic Excitations](https://mlsanity.com/paper/2001.06848/) (2020)
- [Autonomous learning of nonlocal stochastic neuron dynamics](https://mlsanity.com/paper/2011.10955/) (2020)

## Abstract (arXiv)

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.