Periodic training of creeping solids
Soft Condensed Matter
2022-06-01 v1 Disordered Systems and Neural Networks
Abstract
We consider disordered solids in which the microscopic elements can deform plastically in response to stresses on them. We show that by driving the system periodically, this plasticity can be exploited to train in desired elastic properties, both in the global moduli and in local "allosteric" interactions. Periodic driving can couple an applied "source" strain to a target strain over a path in the energy landscape. This coupling allows control of the system's response even at large strains well into the nonlinear regime, where it can be difficult to achieve control simply by design.
Keywords
Cite
@article{arxiv.1909.03528,
title = {Periodic training of creeping solids},
author = {Daniel Hexner and Andrea J. Liu and Sidney R. Nagel},
journal= {arXiv preprint arXiv:1909.03528},
year = {2022}
}