Approximate continuous data assimilation of the 2D Navier-Stokes equations via the Voigt-regularization with observable data

TL;DR

This paper introduces a data assimilation method for the 2D Navier-Stokes equations using Voigt regularization and observational data, proving global well-posedness and error bounds that decay over time.

Contribution

It adapts the Azouani-Olson-Titi algorithm to the Navier-Stokes-Voigt equations driven by observational data, establishing new theoretical error bounds.

Findings

Global well-posedness of the new assimilation system.

Error bounds decay exponentially and algebraically over time.

Large-time error approaches zero as the regularization parameter decreases.

Abstract

We propose a data assimilation algorithm for the 2D Navier-Stokes equations, based on the Azouani, Olson, and Titi (AOT) algorithm, but applied to the 2D Navier-Stokes-Voigt equations. Adapting the AOT algorithm to regularized versions of Navier-Stokes has been done before, but the innovation of this work is to drive the assimilation equation with observational data, rather than data from a regularized system. We first prove that this new system is globally well-posed. Moreover, we prove that for any admissible initial data, the $L^2$ and $H^1$ norms of error are bounded by a constant times a power of the Voigt-regularization parameter $\alpha>0$, plus a term which decays exponentially fast in time. In particular, the large-time error goes to zero algebraically as $\alpha$ goes to zero. Assuming more smoothness on the initial data and forcing, we also prove similar results for the $H^2$ norm.