Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Mathematics > Numerical Analysis

arXiv:1807.07393 (math)
[Submitted on 19 Jul 2018 (v1), last revised 14 May 2019 (this version, v2)]

Title:A finite difference approximation of a two dimensional time fractional advection-dispersion problem

Authors:Carlos E. Mejía, Alejandro Piedrahita
View PDF
Abstract:Time fractional advection-dispersion equations arise as generalizations of classical integer order advection-dispersion equations and are increasingly used to model fluid flow problems through porous media. In this paper we develop an implicit finite difference method to solve a two-dimensional initial boundary value problem for the linear time fractional advection-dispersion equation with variable coefficients on a bounded domain. Consistency, stability and convergence of the method are proved in detail and the numerical experiments offer a good insight into the quality of the obtained approximations.
Subjects: Numerical Analysis (math.NA)
Cite as: arXiv:1807.07393 [math.NA]
  (or arXiv:1807.07393v2 [math.NA] for this version)
  https://doi.org/10.48550/arXiv.1807.07393

Submission history

From: Alejandro Piedrahita [view email]
[v1] Thu, 19 Jul 2018 13:24:02 UTC (453 KB)
[v2] Tue, 14 May 2019 21:56:46 UTC (457 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
view license
Current browse context:
math.NA
< prev   |   next >
new | recent | 2018-07
Change to browse by:
math

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)