Metamaterial-Based Analog Recurrent Neural Network Toward Machine

Wave-based analog computation platforms hold the promise of realizing intelligent information processing with high speed and low computational cost, but current design principles face intractable problems in processing low-frequency wave information end to end at the physical layer. Here we theoretically demonstrate a concept of a metamaterial-based analog recurrent neural network (Meta-RNN) for intelligent classification of physical information carried by mechanical vibrations. With the spatiotemporal recurrence relation induced by coupled local resonators of the simulated metamaterial model, the MetaRNN is capable of memorizing the internal hidden state in the dynamic displacement field. The trained coupling and synergy of local resonators lead to desirable vibration energy localization with selective frequency extraction capability, which can distinguish the intrinsic characteristics of vibration information. The proposed Meta-RNN provides a foundation for implementing a promising mechanical analog processor for intelligent vibration information processing and classification, and paves the way to efficient machine learning platforms for machine intelligence.