Limits of multifunctionality in tunable networks
Abstract
Nature is rife with networks that are functionally optimized to propagate inputs to perform specific tasks. Whether via genetic evolution or dynamic adaptation, many networks create functionality by locally tuning interactions between nodes. Here we explore this behavior in two contexts: strain propagation in mechanical networks and pressure redistribution in flow networks. By adding and removing links, we are able to optimize both types of networks to perform specific functions. We define a single function as a tuned response of a single “target” link when another, predetermined part of the network is activated. Using network structures generated via such optimization, we investigate how many simultaneous functions such networks can be programed to fulfill. We find that both flow and mechanical networks display qualitatively similar phase transitions in the number of targets that can be tuned, along with the same robust finite-size scaling behavior. We discuss how these properties can be understood in the context of constraint–satisfaction problems.
- Authors:
- Publication Date:
- Research Org.:
- Univ. of Pennsylvania, Philadelphia, PA (United States); Univ. of Chicago, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Materials Sciences & Engineering Division; Simons Foundation; National Science Foundation (NSF)
- OSTI Identifier:
- 1492086
- Alternate Identifier(s):
- OSTI ID: 1596603
- Grant/Contract Number:
- FG02-05ER46199; FG02-03ER46088; DMR-1506625; PHY-1554887
- Resource Type:
- Published Article
- Journal Name:
- Proceedings of the National Academy of Sciences of the United States of America
- Additional Journal Information:
- Journal Name: Proceedings of the National Academy of Sciences of the United States of America Journal Volume: 116 Journal Issue: 7; Journal ID: ISSN 0027-8424
- Publisher:
- National Academy of Sciences
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY; mechanical networks; mechanical metamaterials; functional materials; allostery; multifunctionality; network optimization; flow networks; constraint–satisfaction problems
Citation Formats
Rocks, Jason W., Ronellenfitsch, Henrik, Liu, Andrea J., Nagel, Sidney R., and Katifori, Eleni. Limits of multifunctionality in tunable networks. United States: N. p., 2019.
Web. doi:10.1073/pnas.1806790116.
Rocks, Jason W., Ronellenfitsch, Henrik, Liu, Andrea J., Nagel, Sidney R., & Katifori, Eleni. Limits of multifunctionality in tunable networks. United States. https://doi.org/10.1073/pnas.1806790116
Rocks, Jason W., Ronellenfitsch, Henrik, Liu, Andrea J., Nagel, Sidney R., and Katifori, Eleni. Thu .
"Limits of multifunctionality in tunable networks". United States. https://doi.org/10.1073/pnas.1806790116.
@article{osti_1492086,
title = {Limits of multifunctionality in tunable networks},
author = {Rocks, Jason W. and Ronellenfitsch, Henrik and Liu, Andrea J. and Nagel, Sidney R. and Katifori, Eleni},
abstractNote = {Nature is rife with networks that are functionally optimized to propagate inputs to perform specific tasks. Whether via genetic evolution or dynamic adaptation, many networks create functionality by locally tuning interactions between nodes. Here we explore this behavior in two contexts: strain propagation in mechanical networks and pressure redistribution in flow networks. By adding and removing links, we are able to optimize both types of networks to perform specific functions. We define a single function as a tuned response of a single “target” link when another, predetermined part of the network is activated. Using network structures generated via such optimization, we investigate how many simultaneous functions such networks can be programed to fulfill. We find that both flow and mechanical networks display qualitatively similar phase transitions in the number of targets that can be tuned, along with the same robust finite-size scaling behavior. We discuss how these properties can be understood in the context of constraint–satisfaction problems.},
doi = {10.1073/pnas.1806790116},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
number = 7,
volume = 116,
place = {United States},
year = {Thu Jan 24 00:00:00 EST 2019},
month = {Thu Jan 24 00:00:00 EST 2019}
}
https://doi.org/10.1073/pnas.1806790116
Web of Science
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