Analysis of continuous data assimilation with large (or even infinite) nudging parameters

TL;DR

This paper demonstrates that continuous data assimilation can achieve long-term optimal accuracy for PDEs like heat and Navier-Stokes equations even with arbitrarily large nudging parameters, improving theoretical understanding and practical implementation.

Contribution

It introduces a new error analysis method that proves optimal long-time accuracy of CDA with large nudging parameters, independent of their size.

Findings

Error bounds are independent of nudging parameter size.

Optimal long-time solutions are achievable with large nudging parameters.

Numerical tests confirm theoretical predictions.

Abstract

This paper considers continuous data assimilation (CDA) in partial differential equation (PDE) discretizations where nudging parameters can be taken arbitrarily large. We prove that long-time optimally accurate solutions are obtained for such parameters for the heat and Navier-Stokes equations (using implicit time stepping methods), with error bounds that do not grow as the nudging parameter gets large. Existing theoretical results either prove optimal accuracy but with the error scaled by the nudging parameter, or suboptimal accuracy that is independent of it. The key idea to the improved analysis is to decompose the error based on a weighted inner product that incorporates the (symmetric by construction) nudging term, and prove that the projection error from this weighted inner product is optimal and independent of the nudging parameter. We apply the idea to BDF2 - finite element discretizations of the heat equation and Navier-Stokes equations to show that with CDA, they will admit optimal long-time accurate solutions independent of the nudging parameter, for nudging parameters large enough. Several numerical tests are given for the heat equation, fluid transport equation, Navier-Stokes, and Cahn-Hilliard that illustrate the theory.