IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-32280-6.html
   My bibliography  Save this article

Everlasting impact of initial perturbations on first-passage times of non-Markovian random walks

Author

Listed:
  • N. Levernier

    (Aix Marseille Univ., Université de Toulon, CNRS, CPT, Turing Center for Living Systems)

  • T. V. Mendes

    (Laboratoire Ondes et Matière d’Aquitaine, University of Bordeaux, Unité Mixte de Recherche 5798, CNRS)

  • O. Bénichou

    (Laboratoire de Physique Théorique de la Matière Condensée, CNRS/UPMC)

  • R. Voituriez

    (Laboratoire de Physique Théorique de la Matière Condensée, CNRS/UPMC
    Laboratoire Jean Perrin, CNRS/UPMC)

  • T. Guérin

    (Laboratoire Ondes et Matière d’Aquitaine, University of Bordeaux, Unité Mixte de Recherche 5798, CNRS)

Abstract

Persistence, defined as the probability that a signal has not reached a threshold up to a given observation time, plays a crucial role in the theory of random processes. Often, persistence decays algebraically with time with non trivial exponents. However, general analytical methods to calculate persistence exponents cannot be applied to the ubiquitous case of non-Markovian systems relaxing transiently after an imposed initial perturbation. Here, we introduce a theoretical framework that enables the non-perturbative determination of persistence exponents of Gaussian non-Markovian processes with non stationary dynamics relaxing to a steady state after an initial perturbation. Two situations are analyzed: either the system is subjected to a temperature quench at initial time, or its past trajectory is assumed to have been observed and thus known. Our theory covers the case of spatial dimension higher than one, opening the way to characterize non-trivial reaction kinetics for complex systems with non-equilibrium initial conditions.

Suggested Citation

  • N. Levernier & T. V. Mendes & O. Bénichou & R. Voituriez & T. Guérin, 2022. "Everlasting impact of initial perturbations on first-passage times of non-Markovian random walks," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-32280-6
    DOI: 10.1038/s41467-022-32280-6
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-32280-6
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-022-32280-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. S. Condamin & O. Bénichou & V. Tejedor & R. Voituriez & J. Klafter, 2007. "First-passage times in complex scale-invariant media," Nature, Nature, vol. 450(7166), pages 77-80, November.
    2. N. Levernier & M. Dolgushev & O. Bénichou & R. Voituriez & T. Guérin, 2019. "Survival probability of stochastic processes beyond persistence exponents," Nature Communications, Nature, vol. 10(1), pages 1-7, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Léo Régnier & Maxim Dolgushev & Olivier Bénichou, 2023. "Record ages of non-Markovian scale-invariant random walks," Nature Communications, Nature, vol. 14(1), pages 1-7, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wijesundera, Isuri & Halgamuge, Malka N. & Nirmalathas, Ampalavanapillai & Nanayakkara, Thrishantha, 2016. "MFPT calculation for random walks in inhomogeneous networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 986-1002.
    2. Khajehnejad, Moein, 2019. "Efficiency of long-range navigation on Treelike fractals," Chaos, Solitons & Fractals, Elsevier, vol. 122(C), pages 102-110.
    3. Huang, Liang & Zheng, Yu, 2023. "Asymptotic formula on APL of fractal evolving networks generated by Durer Pentagon," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    4. A. Barbier-Chebbah & O. Bénichou & R. Voituriez & T. Guérin, 2024. "Long-term memory induced correction to Arrhenius law," Nature Communications, Nature, vol. 15(1), pages 1-7, December.
    5. O’Keeffe, Kevin & Santi, Paolo & Wang, Brandon & Ratti, Carlo, 2021. "Urban sensing as a random search process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
    6. Zhao, Dan & Ji, Shou-feng & Wang, He-ping & Jiang, Li-wen, 2021. "How do government subsidies promote new energy vehicle diffusion in the complex network context? A three-stage evolutionary game model," Energy, Elsevier, vol. 230(C).
    7. Huang, Wei & Chen, Shengyong & Wang, Wanliang, 2014. "Navigation in spatial networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 132-154.
    8. Henrik Seckler & Ralf Metzler, 2022. "Bayesian deep learning for error estimation in the analysis of anomalous diffusion," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    9. Telcs, András & Csernai, Márton & Gulyás, András, 2013. "Load balanced diffusive capture process on homophilic scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(3), pages 510-519.
    10. Le, Anbo & Gao, Fei & Xi, Lifeng & Yin, Shuhua, 2015. "Complex networks modeled on the Sierpinski gasket," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 646-657.
    11. Chełminiak, Przemysław, 2024. "First-passage time statistics for non-linear diffusion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    12. Zhang, Jingyuan & Xiang, Yonghong & Sun, Weigang, 2018. "A discrete random walk on the hypercube," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 1-7.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-32280-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.