Statistically stationary solutions to the stochastic isentropic compressible Euler equations with linear damping

TL;DR

This paper proves the existence of statistically stationary solutions for the one-dimensional stochastic isentropic compressible Euler equations with linear damping driven by noise, using a multi-level approximation and novel entropy bounds.

Contribution

It introduces a new approach with multi-level approximation and uniform entropy bounds to establish stationary solutions for stochastic Euler equations.

Findings

Existence of invariant measures for approximate systems.

Construction of stationary solutions via limit processes.

Novel entropy bounds enabling passage to the limit.

Abstract

We study the long time behavior of isentropic compressible Euler equations with linear damping driven by a white-in-time noise, on a one-dimensional torus. We prove the existence of a statistically stationary solution in the class of weak martingale entropy solutions for any adiabatic constant $\gamma>1$, which satisfies an associated entropy inequality. To establish this result, we use a multi-level approximation scheme consisting of a truncation parameter $R$ and an artificial viscosity parameter $\epsilon$. The truncated system preserves the structure of the regularized system with the artificial viscosity, thereby providing key properties such as an invariant region and non-existence of vacuum at the approximate level. These properties allow us to construct an invariant measure for the approximate system in both $R$ and $\epsilon$ associated to a Feller semigroup for the well-posed dynamics of the approximate system for any $\gamma > 1$. This gives us a statistically stationary solution for the approximate problem, which we then successively pass to the limit as $R \to \infty$ and as $\epsilon \to 0$ to obtain a statistically stationary solution to the original stochastic system. Our analysis is novel, using new techniques for establishing uniform bounds on entropies of all orders, which allow us to pass to the limit in the parameters. We believe that this result is a valuable step towards further understanding the long-time statistical behavior of the stochastic Euler equations in one spatial dimension.