2021
DOI: 10.48550/arxiv.2107.11289
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Nonlocal cross-interaction systems on graphs: Nonquadratic Finslerian structure and nonlinear mobilities

Abstract: In this work we study a two-species nonlocal interaction equation on a graph. We provide a rigorous interpretation of the interaction system as a gradient flow in a Finsler setting, extending the recent work of Esposito et. al.,[34], to the case of multiple species. Additionally, we provide a characterization of critical points of the corresponding energy functional which are also stationary state of the dynamics. Depending on (rather general) conditions on the interaction kernels, we are able to distinguish b… Show more

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Cited by 4 publications
(21 citation statements)
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“…This free energy was proposed in the above literature to derive the long-time dynamics of the system in dimension d = 2. We also refer to [6,14,16,34] for other recent multi-species models in the literature where our methods could be applied.…”
Section: Introductionmentioning
confidence: 99%
“…This free energy was proposed in the above literature to derive the long-time dynamics of the system in dimension d = 2. We also refer to [6,14,16,34] for other recent multi-species models in the literature where our methods could be applied.…”
Section: Introductionmentioning
confidence: 99%
“…In order to cope with this structural property, one needs to consider suitable interpolating functions so that to be able to describe the flux, hence the dynamics. We refer to [29,28,40] for more details, as well as related works [54,56,16]. Another important aspect is to deal with a large number of entities, for instance individuals or data; hence it is crucial to consider discrete and continuum models.…”
mentioning
confidence: 99%
“…The relation constituting the flux depends on a -finite absolutely continuous measure ∈ M + (R ), as in [29,40,28], wherein acts as an abstract notion of vertices of a graph. More precisely, we associate to a nonlocal time-dependent velocity field : → R the induced flux by using an upwind interpolation as follows d ( , ) = ( , ) + d( ⊗ ) ( , ) − ( , ) − d( ⊗ ) ( , ).…”
mentioning
confidence: 99%
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