Limits of multifunctionality in tunable networks
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.
- Research Organization:
- Univ. of Pennsylvania, Philadelphia, PA (United States); Univ. of Chicago, IL (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Materials Sciences & Engineering Division; Simons Foundation; National Science Foundation (NSF)
- Grant/Contract Number:
- FG02-05ER46199; FG02-03ER46088; DMR-1506625; PHY-1554887
- OSTI ID:
- 1492086
- Alternate ID(s):
- OSTI ID: 1596603
- Journal Information:
- Proceedings of the National Academy of Sciences of the United States of America, Journal Name: Proceedings of the National Academy of Sciences of the United States of America Vol. 116 Journal Issue: 7; ISSN 0027-8424
- Publisher:
- National Academy of SciencesCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
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