2021/03/15 by Daniel Hexner, Hexner, Daniel · 1 citation
Engineering · Mathematics · Neuroscience · Physics and Astronomy · #Adhesion, Friction, and Surface Interactions #Advanced Materials and Mechanics #Computer science #Engineering #FOS: Physical sciences #Function (biology) #Geometry #Materials science #Mathematics #Metamaterial #Optoelectronics #Process (computing) #Reduction (mathematics) #Retraining #Soft Condensed Matter (cond-mat.soft) #Tactile and Sensory Interactions #Task (project management) #cond-mat.soft
paper · pdf · doi:10.48550/arxiv.2103.08235
published in arXiv (Cornell University) (Cornell University)
arxiv created 2021/03/15 · openalex publication_date 2021/03/15 · arxiv updated 2021/03/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
Elastic metamaterials are often designed for a single permanent function. We explore the possibility of altering a material's function repeatedly through a self-organization, "training" process, controlled by applied strains. We show that the elastic function can be altered numerous times, though each new trained task imprints a memory. This ultimately leads to material degradation through the gradual reduction of the frequency gap in the density of states. We also show that retraining adapts previously trained low energy modes to a new function. As a result consecutive trained responses are realized similarly. We show how retraining can be exploited to attain a response that would otherwise be difficult.