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Topologically controlled emergent dynamics in flow networks

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arxiv 2001.01811 v2 pith:R76WI6TW submitted 2020-01-06 cond-mat.soft cond-mat.dis-nnnlin.PS

classification cond-mat.softcond-mat.dis-nnnlin.PS
keywords networksdynamicscomplexflowcontrolledemergingexcitableoscillatory
verification ladder T0 review T1 audit T2 compute T3 formal
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Flow networks are essential for both living organisms and enginneered systems. These networks often present complex dynamics controlled, at least in part, by their topology. Previous works have shown that topologically complex networks interconnecting explicitly oscillatory or excitable elements can display rich emerging dynamics. Here we present a model for complex flow networks with non-linear conductance that allows for internal accumulation/depletion of volume, without any inherent oscillatory or excitable behavior at the nodes. In the absence of any time dependence in the pressure input and output we observe emerging dynamics in the form of self-sustained waves, which travel through the system. The frequency of these waves depends strongly on the network architecture and it can be explained with a topological metric.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. On the rectification of oscillatory flows by flexible leaflets in a confined geometry

    physics.flu-dyn 2026-07 conditional novelty 6.0 of 10

    Collective flexible leaflets rectify low-Re oscillatory squeeze flow, maximizing net transport at high density and an optimal elastoviscous number η.

  2. Does the brain behave like a (complex) network? I. Dynamics

    q-bio.NC 2024-12 unverdicted novelty 4.0 of 10

    A review arguing that the brain's network structure is an empirical hypothesis about dynamic relevance, not an established fact.

  3. Biological detail and graph structure in network neuroscience

    q-bio.NC 2025-07 conditional novelty 2.0 of 10

    A review arguing that generalizing network structure does not escape the fundamental problems of intrinsicality, universality, and functional meaningfulness that already affect standard brain network models.

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