用单个气泡的混沌振动实现高效计算，突破传统神经网络依赖。
Reservoir Computing with a Single Oscillating Gas Bubble: Emphasizing the Chaotic Regime
- 利用声波驱动气泡非线性振荡，构建物理型储层计算系统。
- 在混沌状态下，系统对时间序列预测和分类任务准确率达高。
- 适合追求低功耗、硬件加速的类脑计算研究者。
人工智能系统的日益增长的计算与能耗需求，推动了基于物理效应的新型软硬件计算方案探索。根据机器学习理论，基于神经网络的计算系统需具备非线性能力，以有效建模复杂模式与关系。为此，大量研究聚焦于各类非线性物理系统以提升神经网络性能。本文提出并理论验证了一种基于液态介质中单个捕获气泡的储层计算系统。通过施加外部声压波同时编码输入信息并激发复杂的非线性动力学，展示了该单气泡储层计算系统在复杂基准时间序列预测与分类任务中的高精度表现。特别地，我们证明气泡振荡的混沌物理态是此类计算中最有效的状态。
原文摘要 · Abstract (English)
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.
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