Efficient numerical techniques for computing the Riesz fractional-order reaction-diffusion models arising in biology. (August 2022)
- Record Type:
- Journal Article
- Title:
- Efficient numerical techniques for computing the Riesz fractional-order reaction-diffusion models arising in biology. (August 2022)
- Main Title:
- Efficient numerical techniques for computing the Riesz fractional-order reaction-diffusion models arising in biology
- Authors:
- Alqhtani, Manal
Owolabi, Kolade M.
Saad, Khaled M.
Pindza, Edson - Abstract:
- Abstract: In this work, the solution of Riesz space fractional partial differential equations of parabolic type is considered. Since fractional-in-space operators have been applied to model anomalous diffusion or dispersion problems in the area of mathematical physics with success, we are motivated in this paper to model the standard Brownian motion with the fractional order operator in the sense of the Riesz derivative. We formulate two viable, efficient and reliable high-order approximation schemes for the Riesz derivative which incorporated both the left- and right-hand sides of the Riemann-Liouville derivatives. The proposed methods are analyzed for both stability and convergence. Finally, the methods are used to explore the dynamic richness of pattern formation in two important fractional reaction-diffusion equations that are still of recurring interest. Experimental results for different values of the fractional parameters are reported.
- Is Part Of:
- Chaos, solitons and fractals. Volume 161(2022)
- Journal:
- Chaos, solitons and fractals
- Issue:
- Volume 161(2022)
- Issue Display:
- Volume 161, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 161
- Issue:
- 2022
- Issue Sort Value:
- 2022-0161-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-08
- Subjects:
- Riesz operator -- Subdiffusion and superdiffusion processes
Chaotic behavior in systems -- Periodicals
Solitons -- Periodicals
Fractals -- Periodicals
Chaotic behavior in systems
Fractals
Solitons
Periodicals
003.7 - Journal URLs:
- http://www.elsevier.com/journals ↗
http://www.sciencedirect.com/science/journal/09600779 ↗ - DOI:
- 10.1016/j.chaos.2022.112394 ↗
- Languages:
- English
- ISSNs:
- 0960-0779
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3129.716000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 22580.xml