Title: Reservoir Computing with a Single Oscillating Gas Bubble: Emphasizing the Chaotic Regime Show Chinese title

Title: 基于单个振荡气泡的储层计算：聚焦于混沌状态

Abstract: 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. Show Chinese abstract

Abstract: 人工智能系统的计算和能耗需求不断增加，促使人们探索利用物理效应进行计算的替代软硬件解决方案。 根据机器学习理论，基于神经网络的计算系统必须表现出非线性，才能有效建模复杂的模式和关系。 这一要求推动了对各种非线性物理系统的广泛研究，以提升神经网络的性能。 本文提出并从理论上验证了一种基于液体中单个气泡的储备池计算系统。 通过施加外部声压波来编码输入信息并激发复杂的非线性动力学，我们展示了这种单气泡储备池计算系统能够以高精度预测复杂的基准时间序列，并完成分类任务。 具体而言，我们证明了气泡振荡的混沌物理状态在这种计算中最有效。