# Reservoir Computing with a Single Oscillating Gas Bubble: Emphasizing the Chaotic Regime

arXiv: https://arxiv.org/abs/2504.07221 · HTML: https://mlsanity.com/paper/2504.07221/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 116 references that are in the map.
- First posted on arXiv 25 Mar 2025.

## Builds on (most cited first, 19 of 19)

- [Next Generation Reservoir Computing](https://mlsanity.com/paper/2106.07688/) (2021)
- [A Systematic Review of Green AI](https://mlsanity.com/paper/2301.11047/) (2023)
- [Theory of neuromorphic computing by waves: machine learning by rogue waves, dispersive shocks, and solitons](https://mlsanity.com/paper/1912.07044/) (2019)
- [Do Reservoir Computers Work Best at the Edge of Chaos?](https://mlsanity.com/paper/2012.01409/) (2020)
- [Analogue and Physical Reservoir Computing Using Water Waves](https://mlsanity.com/paper/2306.09095/) (2023)
- [Learning dynamical systems from data: A simple cross-validation perspective, part III: Irregularly-Sampled Time Series](https://mlsanity.com/paper/2111.13037/) (2021)
- [A perspective on physical reservoir computing with nanomagnetic devices](https://mlsanity.com/paper/2212.04851/) (2022)
- [TE-ESN: Time Encoding Echo State Network for Prediction Based on Irregularly Sampled Time Series Data](https://mlsanity.com/paper/2105.00412/) (2021)
- [Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective](https://mlsanity.com/paper/2407.04717/) (2024)
- [Reservoir Computing with Noise](https://mlsanity.com/paper/2303.00585/) (2023)
- [Reservoir Computing Using Measurement-Controlled Quantum Dynamics](https://mlsanity.com/paper/2403.01024/) (2024)
- [Neural Echo State Network using oscillations of gas bubbles in water](https://mlsanity.com/paper/2112.11592/) (2021)
- [Efficient Design of Hardware-Enabled Reservoir Computing in FPGAs](https://mlsanity.com/paper/1805.03033/) (2018)
- [A New Paradigm of Reservoir Computing Exploiting Hydrodynamics](https://mlsanity.com/paper/2302.01978/) (2023)
- [Reservoir computing based on solitary-like waves dynamics of film flows: a proof of concept](https://mlsanity.com/paper/2303.01801/) (2023)
- [Physical Reservoir Computing Enabled by Solitary Waves and Biologically-Inspired Nonlinear Transformation of Input Data](https://mlsanity.com/paper/2402.03319/) (2024)
- [Efficient Optimisation of Physical Reservoir Computers using only a Delayed Input](https://mlsanity.com/paper/2401.14371/) (2024)
- [Modular DFR: Digital Delayed Feedback Reservoir Model for Enhancing Design Flexibility](https://mlsanity.com/paper/2307.11094/) (2023)
- [Comparative Analysis of Predicting Subsequent Steps in H'enon Map](https://mlsanity.com/paper/2405.10190/) (2024)

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

- [TunnElQNN: A Hybrid Quantum-classical Neural Network for Efficient Learning](https://mlsanity.com/paper/2505.00933/) (2025)
- [Resonant-Tunnelling Diode Reservoir Computing System for Image Recognition](https://mlsanity.com/paper/2507.15158/) (2025)

## Abstract (arXiv)

The rising computational and energy demands of artificial intelligence systems urge the exploration of alternative software and hardware solutions that exploit physical effects for computation. According to machine learning theory, a neural network-based computational system must exhibit nonlinearity to effectively model complex patterns and relationships. This requirement has driven extensive research into various nonlinear physical systems to enhance the performance of neural networks. In this paper, we propose and theoretically validate a reservoir computing system based on a single bubble trapped within a bulk of liquid. By applying an external acoustic pressure wave to both encode input information and excite the complex nonlinear dynamics, we showcase the ability of this single-bubble reservoir computing system to forecast complex benchmarking time series and undertake classification tasks with high accuracy. Specifically, we demonstrate that a chaotic physical regime of bubble oscillation proves to be the most effective for this kind of computations.