# Detecting disturbances in network-coupled dynamical systems with machine learning

arXiv: https://arxiv.org/abs/2307.12771 · HTML: https://mlsanity.com/paper/2307.12771/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 40 references that are in the map.
- First posted on arXiv 24 Jul 2023.

## Builds on (most cited first, 10 of 10)

- [Using Machine Learning to Anticipate Tipping Points and Extrapolate to Post-Tipping Dynamics of Non-Stationary Dynamical Systems](https://mlsanity.com/paper/2207.00521/) (2022)
- [Parallel Machine Learning for Forecasting the Dynamics of Complex Networks](https://mlsanity.com/paper/2108.12129/) (2021)
- [Model-Free Control of Dynamical Systems with Deep Reservoir Computing](https://mlsanity.com/paper/2010.02285/) (2020)
- [Using Machine Learning to Assess Short Term Causal Dependence and Infer Network Links](https://mlsanity.com/paper/1912.02721/) (2019)
- [Reservoir Computing with Noise](https://mlsanity.com/paper/2303.00585/) (2023)
- [Reservoir Computers with Random and Optimized Time-Shifts](https://mlsanity.com/paper/2108.12765/) (2021)
- [Data-driven Localization and Estimation of Disturbance in the Interconnected Power System](https://mlsanity.com/paper/1806.01318/) (2018)
- [Stability Analysis of Reservoir Computers Dynamics via Lyapunov Functions](https://mlsanity.com/paper/1908.04411/) (2019)
- [Time-shift selection for reservoir computing using a rank-revealing QR algorithm](https://mlsanity.com/paper/2211.17095/) (2022)
- [Suppressing unknown disturbances to dynamical systems using machine learning](https://mlsanity.com/paper/2307.03690/) (2023)

## Abstract (arXiv)

Identifying disturbances in network-coupled dynamical systems without knowledge of the disturbances or underlying dynamics is a problem with a wide range of applications. For example, one might want to know which nodes in the network are being disturbed and identify the type of disturbance. Here we present a model-free method based on machine learning to identify such unknown disturbances based only on prior observations of the system when forced by a known training function. We find that this method is able to identify the locations and properties of many different types of unknown disturbances using a variety of known forcing functions. We illustrate our results both with linear and nonlinear disturbances using food web and neuronal activity models. Finally, we discuss how to scale our method to large networks.