Issue |
EPL
Volume 136, Number 4, November 2021
|
|
---|---|---|
Article Number | 40002 | |
Number of page(s) | 7 | |
Section | General | |
DOI | https://doi.org/10.1209/0295-5075/ac4dd4 | |
Published online | 04 March 2022 |
Selfsimilar stochastic differential equations
Tel Aviv University Raymond and Beverly Sackler Faculty of Exact Sciences, School of Chemistry Tel Aviv, 69978, Israel
(a) eliazar@tauex.tau.ac.il (corresponding author)
Received: 17 September 2021
Accepted: 19 January 2022
Diffusion in a logarithmic potential (DLP) attracted significant interest in physics recently. The dynamics of DLP are governed by a Langevin stochastic differential equation (SDE) whose underpinning potential is logarithmic, and that is driven by Brownian motion. The SDE that governs DLP is a particular case of a selfsimilar SDE: one that is driven by a selfsimilar motion, and that produces a selfsimilar motion. This paper establishes the pivotal role of selfsimilar SDEs via two novel universality results. I) Selfsimilar SDEs emerge universally, on the macro level, when applying scaling limits to micro-level SDEs. II) Selfsimilar SDEs emerge universally when applying the Lamperti transformation to stationary SDEs. Using the universality results, this paper further establishes: a novel statistical-analysis approach to selfsimilar Ito diffusions; and the focal importance of DLP.
© 2022 The author(s)
Published by the EPLA under the terms of the Creative Commons Attribution 4.0 International License (CC-BY). Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.