4.6 Article

Numerical solutions for asymmetric Levy flights

Journal

NUMERICAL ALGORITHMS
Volume 87, Issue 3, Pages 967-999

Publisher

SPRINGER
DOI: 10.1007/s11075-020-00995-6

Keywords

Levy flights; Finite difference method; Fractional differential equations; Riemann-Liouville derivatives

Funding

  1. Portuguese Government through FCT/MCTES
  2. FCT under POCH funds [PD/BD/142955/2018]
  3. European Social Fund from MEC
  4. Portuguese National Funds from MEC
  5. Centre forMathematics of the University of Coimbra [UIDB/00324/2020]
  6. Fundação para a Ciência e a Tecnologia [PD/BD/142955/2018] Funding Source: FCT

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Levy flights are generalized random walk processes with long-tailed alpha-stable jump length distribution. Numerical methods for α>1 have been widely discussed, but less attention has been paid to the case of 0<α<1. Numerical experiments highlight the advantages and disadvantages of different approaches in obtaining approximations of probability density functions.
Levy flights are generalised random walk processes where the independent stationary increments are drawn from a long-tailed alpha-stable jump length distribution. We consider the formulation of Levy flights, for 0 < 1, in terms of a space-fractional diffusion equation which fundamental solutions are the probability density functions. First, we present how to obtain the governing equation of Levy motion from the Fourier transform of the jump distribution. Then, we derive a family of implicit numerical methods to determine the numerical solutions and we study their consistency and stability. Although numerical algorithms for the case 1 < 2 have been widely discussed, very few works paid attention to the case we discuss here. We present numerical experiments to show the performance of the numerical methods and to highlight the advantages and disadvantages of the different approaches. In the end we determine the numerical solutions of an initial value problem, that considers an approximation of the Dirac delta function as the initial condition, in order to obtain approximations of the probability density functions.

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