Parabolic Anderson model in a dynamic random environment: random conductances
Dirk Erhard, Frank den Hollander, Gregory Maillard

TL;DR
This paper studies the parabolic Anderson model with dynamic random conductances, deriving formulas for Lyapunov exponents that depend on the distribution of conductance values, highlighting the influence of local conductance pockets on growth rates.
Contribution
It extends the analysis of the parabolic Anderson model to include random conductances, providing explicit formulas for annealed Lyapunov exponents and bounds for quenched exponents.
Findings
Annealed Lyapunov exponents are given by the supremum over conductance support.
Quenched Lyapunov exponent is bounded below by the same supremum.
Lyapunov exponents are influenced by regions where conductances are near the maximizing value.
Abstract
The parabolic Anderson model is defined as the partial differential equation \partial u(x,t)/\partial t = \kappa\Delta u(x,t) + \xi(x,t)u(x,t), x\in\Z^d, t\geq 0, where \kappa \in [0,\infty) is the diffusion constant, \Delta is the discrete Laplacian, and \xi is a dynamic random environment that drives the equation. The initial condition u(x,0)=u_0(x), x\in\Z^d, is taken to be non-negative and bounded. The solution of the parabolic Anderson equation describes the evolution of a field of particles performing independent simple random walks with binary branching: particles jump at rate 2d\kappa, split into two at rate \xi \vee 0, and die at rate (-\xi) \vee 0. Our focus is on the Lyapunov exponents \lambda_p(\kappa) = \lim_{t\to\infty} \frac{1}{t} \log \E([u(0,t)]^p)^{1/p}, p \in \N, and \lambda_0(\kappa) = \lim_{t\to\infty} \frac{1}{t}\log u(0,t). We investigate what happens when…
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Taxonomy
TopicsStochastic processes and statistical mechanics · stochastic dynamics and bifurcation · Theoretical and Computational Physics
