Artificial neural networks (ANNs), which are inspired by the brain, are a central pillar in the ongoing breakthrough in artificial intelligence. In recent years, researchers have examined mechanical implementations of ANNs, denoted as Physical Neural Networks (PNNs). PNNs offer the opportunity to view common materials and physical phenomena as networks, and to associate computational power with them. In this work, we incorporated mechanical bistability into PNNs, enabling memory and a direct link between computation and physical action. To achieve this, we consider an interconnected network of bistable liquid-filled chambers. We first map all possible equilibrium configurations or steady states, and then examine their stability. Building on these maps, both global and local algorithms for training multistable PNNs are implemented. These algorithms enable us to systematically examine the network's capability to achieve stable output states and thus the network's ability to perform computational tasks. By incorporating PNNs and multistability, we can design structures that mechanically perform tasks typically associated with electronic neural networks, while directly obtaining physical actuation. The insights gained from our study pave the way for the implementation of intelligent structures in smart tech, metamaterials, medical devices, soft robotics, and other fields.

翻译：受大脑启发的神经网络（ANNs）是当前人工智能突破的核心支柱。近年来，研究人员探索了神经网络的力学实现形式，称为物理神经网络（PNNs）。PNNs 提供了将常见材料与物理现象视为网络的可能性，并赋予其计算能力。本研究将力学双稳态特性引入 PNNs，实现了记忆功能并建立了计算与物理行为的直接关联。为实现这一目标，我们构建了由双稳态液腔单元互联构成的网络系统。首先，我们绘制了所有可能的平衡构型（稳态），并分析了其稳定性。基于这些构型图谱，我们实现了针对多稳态 PNNs 的全局与局部训练算法。这些算法使我们能够系统评估网络实现稳定输出状态的能力，进而验证其执行计算任务的性能。通过融合 PNNs 与多稳态特性，我们能够设计出以机械方式执行传统电子神经网络任务的结构，同时直接获得物理驱动效果。本研究的发现为智能技术、超材料、医疗设备、软体机器人等领域的智能结构实现开辟了新路径。