  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.
