A regularity method for lower bounds on the Lyapunov exponent for stochastic differential equations

TL;DR

This paper introduces a novel method combining an identity relating Lyapunov exponents to Fisher information and a quantitative hypoelliptic regularity theory to establish lower bounds on the top Lyapunov exponent for stochastic differential equations, including the Lorenz 96 model.

Contribution

It presents a new approach for lower bounds on Lyapunov exponents using Fisher information and hypoelliptic regularity, applicable to complex systems like the Lorenz 96 model.

Findings

Proves positivity of the top Lyapunov exponent for certain weakly-dissipative SDEs.

Applies the method to the Lorenz 96 model in any dimension.

Establishes a new connection between Lyapunov exponents and Fisher information in stochastic systems.

Abstract

We put forward a new method for obtaining quantitative lower bounds on the top Lyapunov exponent of stochastic differential equations (SDEs). Our method combines (i) an (apparently new) identity connecting the top Lyapunov exponent to a Fisher information-like functional of the stationary density of the Markov process tracking tangent directions with (ii) a novel, quantitative version of H\"ormander's hypoelliptic regularity theory in an $L^1$ framework which estimates this (degenerate) Fisher information from below by a $W^{s,1}_{\mathrm{loc}}$ Sobolev norm. This method is applicable to a wide range of systems beyond the reach of currently existing mathematically rigorous methods. As an initial application, we prove the positivity of the top Lyapunov exponent for a class of weakly-dissipative, weakly forced SDE; in this paper we prove that this class includes the Lorenz 96 model in any dimension, provided the additive stochastic driving is applied to any consecutive pair of modes.