Brownian motion in non-equilibrium systems and the Ornstein-Uhlenbeck stochastic process.

Sci Rep

Instituto de Física, Universidad Autónoma de San Luis Potosí, Alvaro Obregón 64, 78000, San Luis Potosí, S.L.P., Mexico.

Published: October 2017

The Ornstein-Uhlenbeck stochastic process is an exact mathematical model providing accurate representations of many real dynamic processes in systems in a stationary state. When applied to the description of random motion of particles such as that of Brownian particles, it provides exact predictions coinciding with those of the Langevin equation but not restricted to systems in thermal equilibrium but only conditioned to be stationary. Here, we investigate experimentally single particle motion in a two-dimensional granular system in a stationary state, consisting of 1 mm stainless balls on a plane circular surface. The motion of the particles is produced by an alternating magnetic field applied perpendicular to the surface of the container. The mean square displacement of the particles is measured for a range of low concentrations and it is found that following an appropriate scaling of length and time, the short-time experimental curves conform a master curve covering the range of particle motion from ballistic to diffusive in accordance with the description of the Ornstein-Uhlenbeck model.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5626708PMC
http://dx.doi.org/10.1038/s41598-017-12737-1DOI Listing

Publication Analysis

Top Keywords

ornstein-uhlenbeck stochastic
8
stochastic process
8
stationary state
8
motion particles
8
particle motion
8
brownian motion
4
motion non-equilibrium
4
non-equilibrium systems
4
systems ornstein-uhlenbeck
4
process ornstein-uhlenbeck
4

Similar Publications

Ornstein-Uhlenbeck Adaptation as a Mechanism for Learning in Brains and Machines.

Entropy (Basel)

December 2024

Department of Machine Learning and Neural Computing, Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6500HB Nijmegen, The Netherlands.

Learning is a fundamental property of intelligent systems, observed across biological organisms and engineered systems. While modern intelligent systems typically rely on gradient descent for learning, the need for exact gradients and complex information flow makes its implementation in biological and neuromorphic systems challenging. This has motivated the exploration of alternative learning mechanisms that can operate locally and do not rely on exact gradients.

View Article and Find Full Text PDF

This manuscript explores the stability theory of several stochastic/random models. It delves into analyzing the stability of equilibrium states in systems influenced by standard Brownian motion and exhibit random variable coefficients. By constructing appropriate Lyapunov functions, various types of stability are identified, each associated with distinct stability conditions.

View Article and Find Full Text PDF

Cancer progression is an evolutionary process driven by the selection of cells adapted to gain growth advantage. We present a formal study on the adaptation of gene expression in subclonal evolution. We model evolutionary changes in gene expression as stochastic Ornstein-Uhlenbeck processes, jointly leveraging the evolutionary history of subclones and single-cell expression data.

View Article and Find Full Text PDF

Given the rapid increase in climate change, investigating the impact of climate change on the transmission mechanism of tick-borne diseases is imperative. In order to fully capture the influence of the seasonal variation of temperature, environmental disturbances and the co-feeding transmission on the spread of tick-borne diseases, we propose a novel stochastic dynamical model that couples the mean-reverting Ornstein-Uhlenbeck temperature equation with periodic input to the tick-borne disease model. Through theoretical analysis, we derive sufficient conditions for the extinction of tick populations and the eradication of tick-borne diseases, as well as the stochastic persistence conditions of the system.

View Article and Find Full Text PDF

We compute the connected two-time correlator of the maximum M_{N}(t) of N independent Gaussian stochastic processes (GSPs) characterized by a common correlation coefficient ρ that depends on the two times t_{1} and t_{2}. We show analytically that this correlator, for fixed times t_{1} and t_{2}, decays for large N as a power law N^{-γ} (with logarithmic corrections) with a decorrelation exponent γ=(1-ρ)/(1+ρ) that depends only on ρ, but otherwise is universal for any GSP. We study several examples of physical processes including the fractional Brownian motion (fBm) with Hurst exponent H and the Ornstein-Uhlenbeck process (OUP).

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!