Multiple transient memories in sheared suspensions: Robustness, structure, and routes to plasticity
Abstract
Multiple transient memories, first 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 multiple 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 and Nagel [Phys. Rev. Lett. 107, 010603 (2011)] 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, focusing 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.
- Authors:
-
- Univ. of Pennsylvania, Philadelphia, PA (United States)
- Univ. of Chicago, IL (United States)
- Publication Date:
- Research Org.:
- Univ. of Chicago, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1596355
- Grant/Contract Number:
- FG02-03ER46088
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
- Additional Journal Information:
- Journal Volume: 88; Journal Issue: 3; Journal ID: ISSN 1539-3755
- Publisher:
- American Physical Society (APS)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 42 ENGINEERING; memory; suspensions; shearing; plasticity
Citation Formats
Keim, Nathan C., Paulsen, Joseph D., and Nagel, Sidney R. Multiple transient memories in sheared suspensions: Robustness, structure, and routes to plasticity. United States: N. p., 2013.
Web. doi:10.1103/PhysRevE.88.032306.
Keim, Nathan C., Paulsen, Joseph D., & Nagel, Sidney R. Multiple transient memories in sheared suspensions: Robustness, structure, and routes to plasticity. United States. https://doi.org/10.1103/PhysRevE.88.032306
Keim, Nathan C., Paulsen, Joseph D., and Nagel, Sidney R. Fri .
"Multiple transient memories in sheared suspensions: Robustness, structure, and routes to plasticity". United States. https://doi.org/10.1103/PhysRevE.88.032306. https://www.osti.gov/servlets/purl/1596355.
@article{osti_1596355,
title = {Multiple transient memories in sheared suspensions: Robustness, structure, and routes to plasticity},
author = {Keim, Nathan C. and Paulsen, Joseph D. and Nagel, Sidney R.},
abstractNote = {Multiple transient memories, first 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 multiple 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 and Nagel [Phys. Rev. Lett. 107, 010603 (2011)] 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, focusing 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.},
doi = {10.1103/PhysRevE.88.032306},
journal = {Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics},
number = 3,
volume = 88,
place = {United States},
year = {Fri Sep 20 00:00:00 EDT 2013},
month = {Fri Sep 20 00:00:00 EDT 2013}
}
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