Multistable Physical Neural Networks

TL;DR

This paper introduces multistable physical neural networks using mechanical bistability in interconnected chambers, enabling memory, computation, and physical actuation for applications in smart tech and robotics.

Contribution

It presents a novel approach to implementing neural network functionalities through mechanical bistability and stability mapping in physical systems.

Findings

Mapped equilibrium configurations and stability of bistable chambers

Developed training algorithms for multistable PNNs

Demonstrated mechanical computation and actuation capabilities

Abstract

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.