Lyapunov exponents in a slow environment

TL;DR

This paper investigates the behavior of Lyapunov exponents in a 1D Anderson model with slowly varying random environment, revealing their positivity, dependence on noise regularity, and convergence to a top eigenvalue as environment changes.

Contribution

It provides a detailed analysis of Lyapunov exponents in a stochastic PDE with slow environmental variation, connecting their behavior to noise regularity and long-term eigenvalue limits.

Findings

Lyapunov exponent is positive for the model.

Near zero environment change rate, the Lyapunov exponent follows a power law.

As environment change rate increases, the Lyapunov exponent converges to the average top eigenvalue.

Abstract

Motivated by the evolution of a population in a slowly varying random environment, we consider the 1D Anderson model on finite volume, with viscosity $ \kappa > 0 $: $$ \partial_{t} u(t,x) = \kappa \Delta u(t,x) + \xi(t, x) u(t,x), \quad u(0, x) = u_{0}(x), \qquad t > 0, x \in \mathbb{T}. $$ The noise $ \xi $ is chosen constant on time intervals of length $ \tau >0 $ and sampled independently after a time $ \tau $. We prove that the Lyapunov exponent $ \lambda (\tau) $ is positive and near $ \tau= 0 $ follows a power law that depends on the regularity on the driving noise. As $ \tau \to \infty $ the Lyapunov exponent converges to the average top eigenvalue of the associated time-independent Anderson model. The proofs make use of a solid control of the projective component of the solution and build on the Furstenberg--Khasminskii and Bou\'e--Dupuis formulas, as well as on Doob's H-transform and on tools from singular stochastic PDEs.