# Control-aware echo state networks (Ca-ESN) for the suppression of extreme events

arXiv: https://arxiv.org/abs/2308.03095 · HTML: https://mlsanity.com/paper/2308.03095/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 6 Aug 2023.

## Builds on (most cited first, 3 of 3)

- [Backpropagation Algorithms and Reservoir Computing in Recurrent Neural Networks for the Forecasting of Complex Spatiotemporal Dynamics](https://mlsanity.com/paper/1910.05266/) (2019)
- [Robust Optimization and Validation of Echo State Networks for learning chaotic dynamics](https://mlsanity.com/paper/2103.03174/) (2021)
- [Data-driven prediction and control of extreme events in a chaotic flow](https://mlsanity.com/paper/2204.11682/) (2022)

## Built on by (most cited first, 2 of 2)

- [Data-Assimilated Model-Based Reinforcement Learning for Partially Observed Chaotic Flows](https://mlsanity.com/paper/2504.16588/) (2025)
- [Data-assimilated model-informed reinforcement learning](https://mlsanity.com/paper/2506.01755/) (2025)

## Abstract (arXiv)

Extreme event are sudden large-amplitude changes in the state or observables of chaotic nonlinear systems, which characterize many scientific phenomena. Because of their violent nature, extreme events typically have adverse consequences, which call for methods to prevent the events from happening. In this work, we introduce the control-aware echo state network (Ca-ESN) to seamlessly combine ESNs and control strategies, such as proportional-integral-derivative and model predictive control, to suppress extreme events. The methodology is showcased on a chaotic-turbulent flow, in which we reduce the occurrence of extreme events with respect to traditional methods by two orders of magnitude. This works opens up new possibilities for the efficient control of nonlinear systems with neural networks.