Neural Echo State Network using oscillations of gas bubbles in water

TL;DR

This paper introduces a bubble-based reservoir computing system that leverages the nonlinear acoustic response of oscillating gas bubbles in water, combined with Echo State Networks, to predict chaotic time series efficiently.

Contribution

It proposes a novel physical reservoir computing system using gas bubble oscillations and demonstrates its effectiveness in chaotic time series forecasting.

Findings

BRC system can forecast chaotic time series accurately.

BRC performs comparably or better than standard ESN.

Numerical simulations confirm the system's plausibility.

Abstract

In the framework of physical reservoir computing (RC), machine learning algorithms designed for digital computers are executed using analog computer-like nonlinear physical systems that can provide energy-efficient computational power for predicting time-dependent quantities that can be found using nonlinear differential equations. We suggest a bubble-based RC (BRC) system that combines the nonlinearity of an acoustic response of a cluster of oscillating gas bubbles in water with a standard Echo State Network (ESN) algorithm that is well-suited to forecast chaotic time series. We confirm the plausibility of the BRC system by numerically demonstrating its ability to forecast certain chaotic time series similarly to or even more accurately than ESN.