Multiple transient memories, originally discovered in charge-density-wave conductors, are a remarkable and initially counterintuitive example of how a system can store information about its driving. In this class of memories, a system can learn multi
ple driving inputs, nearly all of which are eventually forgotten despite their continual input. If sufficient noise is present, the system regains plasticity so that it can continue to learn new memories indefinitely. Recently, Keim & Nagel showed how multiple transient memories could be generalized to a generic driven disordered system with noise, giving as an example simulations of a simple model of a sheared non-Brownian suspension. Here, we further explore simulation models of suspensions under cyclic shear, focussing on three main themes: robustness, structure, and overdriving. We show that multiple transient memories are a robust feature independent of many details of the model. The steady-state spatial distribution of the particles is sensitive to the driving algorithm; nonetheless, the memory formation is independent of such a change in particle correlations. Finally, we demonstrate that overdriving provides another means for controlling memory formation and retention.
The question of how a disordered materials microstructure translates into macroscopic mechanical response is central to understanding and designing materials like pastes, foams and metallic glasses. Here, we examine a 2D soft jammed material under cy
clic shear, imaging the structure of ~50,000 particles. Below a certain strain amplitude, the structure becomes conserved at long times, while above, it continually rearranges. We identify the boundary between these regimes as a yield strain, defined without rheological measurement. Its value is consistent with a simultaneous but independent measurement of yielding by stress-controlled bulk rheometry. While there are virtually no irreversible rearrangements in the steady state below yielding, we find a largely stable population of plastic rearrangements that are reversed with each cycle. These results point to a microscopic view of mechanical properties under cyclic deformation.
Out-of-equilibrium disordered systems may form memories of external driving in a remarkable fashion. The system remembers multiple values from a series of training inputs yet forgets nearly all of them at long times despite the inputs being continual
ly repeated. Here, learning and forgetting are inseparable aspects of a single process. The memory loss may be prevented by the addition of noise. We identify a class of systems with this behavior, giving as an example a model of non-brownian suspensions under cyclic shear.
