Effect of stochastic resetting on Brownian motion with stochastic diffusion coefficient
Ion Santra, Urna Basu, Sanjib Sabhapandit

TL;DR
This paper investigates the effects of stochastic resetting on Brownian motion with a randomly evolving diffusion coefficient, revealing unique stationary distributions and growth dynamics across different dimensions.
Contribution
It introduces a model of Brownian motion with stochastic diffusion and resetting, deriving exact stationary distributions and analyzing the growth of the stationary region over time.
Findings
Stationary position distribution has a logarithmic divergence at the origin in 1D.
Higher dimensions show a finite constant at the origin, computed exactly.
Inner stationary region grows quadratically with time, faster than ordinary Brownian motion.
Abstract
We study the dynamics of a Brownian motion with a diffusion coefficient which evolves stochastically. We first study this process in arbitrary dimensions and find the scaling form and the corresponding scaling function of the position distribution. We find that the tails of the distribution have exponential tails with a ballistic scaling. We then introduce the resetting dynamics where, at a constant rate, both the position and the diffusion coefficient are reset to zero. This eventually leads to a nonequilibrium stationary state, which we study in arbitrary dimensions. In stark contrast to ordinary Brownian motion under resetting, the stationary position distribution in one dimension has a logarithmic divergence at the origin. For higher dimensions, however, the divergence disappears and the distribution attains a dimension-dependent constant value at the origin, which we compute…
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