2016
DOI: 10.1103/physreve.93.020101
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Single integrodifferential wave equation for a Lévy walk

Abstract: We derive the single integrodifferential wave equation for the probability density function of the position of a classical one-dimensional Lévy walk with continuous sample paths. This equation involves a classical wave operator together with memory integrals describing the spatiotemporal coupling of the Lévy walk. It is valid at all times, not only in the long time limit, and it does not involve any large-scale approximations. It generalizes the well-known telegraph or Cattaneo equation for the persistent rand… Show more

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Cited by 30 publications
(58 citation statements)
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“…This result finds analytical support in Ref. [42] in the strong ballistic case (α < 2). When the transport process is superdiffusive (2 < α < 3), it should similarly hold, due to the large statistical weight of events characterized by particles keeping their velocity for a very long time.…”
Section: Extension To Levy Walkssupporting
confidence: 86%
“…This result finds analytical support in Ref. [42] in the strong ballistic case (α < 2). When the transport process is superdiffusive (2 < α < 3), it should similarly hold, due to the large statistical weight of events characterized by particles keeping their velocity for a very long time.…”
Section: Extension To Levy Walkssupporting
confidence: 86%
“…While for subdiffusion fractional diffusion equations have been derived based on subordination or continuous time random walk theory [3,11], this problem turned out to be much more nontrivial for Lévy walkers due to the spatiotemporal coupling imposing finite velocities [10]. Only very recently progress was made by deriving an integrodifferential wave equation for a onedimensional LW [13]. More generally, in position and velocity space a fractional Klein-Kramers equation containing an n-dimensional correlated LW as a special case was given in Refs.…”
Section: Introductionmentioning
confidence: 99%
“…To implement nonlinear effects we use the structural density approach together with a population density-dependent turning rate. This method has been used by the authors for the analysis of subdiffusive random walks [40,41] and Lévy walks [42,43].…”
mentioning
confidence: 99%