多稳物理神经网络

Eran Ben-Haim, Sefi Givli, Yizhar Or, Amir Gat
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引用次数: 0

摘要

人工神经网络(ANN)的灵感来源于大脑,是人工智能领域不断取得突破的核心支柱。近年来,研究人员开始研究人工神经网络的机械实现,即物理神经网络(PNN)。物理神经网络提供了将常见材料和物理现象视为网络,并将计算能力与之相关联的机会。在这项工作中,我们将机械可变性纳入了物理神经网络,从而实现了记忆以及计算与物理作用之间的直接联系。为此,我们考虑了一个由充满液体的可变腔室组成的互连网络。我们首先绘制了所有可能的平衡配置或稳态图,然后研究了它们的稳定性。在这些映射的基础上,我们实现了训练多稳态 PNN 的全局和局部算法。这些算法使我们能够系统地检验网络实现稳定输出状态的能力,从而检验网络执行计算任务的能力。通过结合 PNN 和多稳定性,我们设计出了能以机械方式执行通常与电子神经网络相关任务的结构,同时直接获得物理驱动力。我们的研究为智能结构在智能技术、超材料、医疗设备、软机器人等领域的应用铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multistable Physical Neural Networks
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.
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