# Suppressing unknown disturbances to dynamical systems using machine learning

arXiv: https://arxiv.org/abs/2307.03690 · HTML: https://mlsanity.com/paper/2307.03690/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 44 references that are in the map.
- First posted on arXiv 19 Jun 2023.

## Builds on (most cited first, 6 of 6)

- [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)
- [Data-driven Localization and Estimation of Disturbance in the Interconnected Power System](https://mlsanity.com/paper/1806.01318/) (2018)
- [Learning unidirectional coupling using echo-state network](https://mlsanity.com/paper/2303.13562/) (2023)

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

- [Detecting disturbances in network-coupled dynamical systems with machine learning](https://mlsanity.com/paper/2307.12771/) (2023)

## Abstract (arXiv)

Identifying and suppressing unknown disturbances to dynamical systems is a problem with applications in many different fields. Here we present a model-free method to identify and suppress an unknown disturbance to an unknown system based only on previous observations of the system under the influence of a known forcing function. We find that, under very mild restrictions on the training function, our method is able to robustly identify and suppress a large class of unknown disturbances. We illustrate our scheme with the identification of both deterministic and stochastic unknown disturbances to an analog electric chaotic circuit and with numerical examples where a chaotic disturbance to various chaotic dynamical systems is identified and suppressed.