Skip to main content
CenXiv.org
This website is in trial operation, support us!
We gratefully acknowledge support from all contributors.
Contribute
Donate
cenxiv logo > cond-mat > arXiv:2110.04851

Help | Advanced Search

Condensed Matter > Statistical Mechanics

arXiv:2110.04851 (cond-mat)
[Submitted on 10 Oct 2021 ]

Title: Anomalous stochastic transport of particles with self-reinforcement and Mittag-Leffler distributed rest times

Title: 具有自增强和 Mittag-Leffler 分布休息时间的粒子异常随机输运

Authors:Daniel Han, Dmitri V. Alexandrov, Anna Gavrilova, Sergei Fedotov
Abstract: We introduce a persistent random walk model for the stochastic transport of particles involving self-reinforcement and a rest state with Mittag-Leffler distributed residence times. The model involves a system of hyperbolic partial differential equations with a non-local switching term described by the Riemann-Liouville derivative. From Monte Carlo simulations, this model generates superdiffusion at intermediate times but reverts to subdiffusion in the long time asymptotic limit. To confirm this result, we derive the equation for the second moment and find that it is subdiffusive in the long time limit. Analyses of two simpler models are also included, which demonstrate the dominance of the Mittag-Leffler rest state leading to subdiffusion. The observation that transient superdiffusion occurs in an eventually subdiffusive system is a useful feature for application in stochastic biological transport.
Abstract: 我们引入了一个持续的随机游走模型,用于涉及自增强和具有Mittag-Leffler分布停留时间的静止状态的粒子随机输运。该模型涉及一个由Riemann-Liouville导数描述的非局部切换项的双曲偏微分方程系统。从蒙特卡罗模拟来看,该模型在中间时间生成超扩散,但在长时间渐近极限下恢复为亚扩散。为了确认这一结果,我们推导了二阶矩的方程,并发现其在长时间极限下是亚扩散的。还包含了对两个更简单模型的分析,这些分析展示了Mittag-Leffler静止状态的主导性导致亚扩散。观察到最终亚扩散系统中出现瞬时超扩散是一个适用于随机生物运输的应用有用特征。
Subjects: Statistical Mechanics (cond-mat.stat-mech) ; Quantitative Methods (q-bio.QM)
Cite as: arXiv:2110.04851 [cond-mat.stat-mech]
  (or arXiv:2110.04851v1 [cond-mat.stat-mech] for this version)
  https://doi.org/10.48550/arXiv.2110.04851
arXiv-issued DOI via DataCite
Related DOI: https://doi.org/10.3390/fractalfract5040221
DOI(s) linking to related resources

Submission history

From: Daniel Han Mr. [view email]
[v1] Sun, 10 Oct 2021 17:02:24 UTC (366 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled
  • View Chinese PDF
  • View PDF
  • TeX Source
license icon view license
Current browse context:
cond-mat.stat-mech
< prev   |   next >
new | recent | 2021-10
Change to browse by:
cond-mat
q-bio
q-bio.QM

References & Citations

  • NASA ADS
  • Google Scholar
  • Semantic Scholar
a export BibTeX citation Loading...

BibTeX formatted citation

×
Data provided by:

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?)
IArxiv Recommender (What is IArxiv?)
  • Author
  • Venue
  • Institution
  • Topic

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?)
  • About
  • Help
  • contact arXivClick here to contact arXiv Contact
  • subscribe to arXiv mailingsClick here to subscribe Subscribe
  • Copyright
  • Privacy Policy
  • Web Accessibility Assistance
  • arXiv Operational Status
    Get status notifications via email or slack

京ICP备2025123034号