In conventional machine learning, a computer is used to minimize a cost function that specifies a desired task, using global information about the entire network. We have now demonstrated how machine learning tasks can be learned and performed without either a computer or global information by taking advantage of physics via a scheme called coupled learning1. Specifically, twin variable-resistor networks are run under different boundary conditions and corresponding edges are compared against only each other for determining resistance updates2. This permits straightforward hardware implementation (see figure) and offers significant scaling advantages in speed and power-consumption for very large networks. Our scheme can potentially be applied to fluidic and mechanical networks. As such, it holds great promise for creating broad new classes of metamaterials that can learn complex and adaptable functionalities.
References: Stern M, Hexner D, Rocks JW, Liu, AJ. Physical Review X 2021; 11, 021045.
https://doi.org/10.1103/PhysRevX.11.021045  Dillavou S, Stern M, Liu AJ, Durian DJ. Physical Review Applied 2022; 18, 014040.