Title: Exponentially convergent data assimilation algorithm for Navier-Stokes equations,
Abstract: The paper presents a new state estimation algorithm for a bilinear equation
representing the Fourier- Galerkin (FG) approximation of the Navier-Stokes (NS)
equations on a torus in R2. This state equation is subject to uncertain but
bounded noise in the input (Kolmogorov forcing) and initial conditions, and its
output is incomplete and contains bounded noise. The algorithm designs a
time-dependent gain such that the estimation error converges to zero
exponentially. The sufficient condition for the existence of the gain are
formulated in the form of algebraic Riccati equations. To demonstrate the
results we apply the proposed algorithm to the reconstruction a chaotic fluid
flow from incomplete and noisy data.