# Application of Gaussian Process Regression to Koopman Mode Decomposition for Noisy Dynamic Data

arXiv: https://arxiv.org/abs/1911.01143 · HTML: https://mlsanity.com/paper/1911.01143/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2019.

## Built on by (most cited first, 1 of 1)

- [Gaussian Process Koopman Mode Decomposition](https://mlsanity.com/paper/2209.04111/) (2022)

## Abstract (arXiv)

Koopman Mode Decomposition (KMD) is a technique of nonlinear time-series analysis that originates from point spectrum of the Koopman operator defined for an underlying nonlinear dynamical system. We present a numerical algorithm of KMD based on Gaussian process regression that is capable of handling noisy finite-time data. The algorithm is applied to short-term swing dynamics of a multi-machine power grid in order to estimate oscillatory modes embedded in the dynamics, and thereby the effectiveness of the algorithm is evaluated.