Nudging is an important data assimilation technique where partial field measurements are
used to control the evolution of a dynamical system and/or to reconstruct the entire phase …

We study the applicability of tools developed by the computer vision community for feature
learning and semantic image inpainting to perform data reconstruction of fluid turbulence …

Motivated by recent progress in data assimilation, we develop an algorithm to dynamically
learn the parameters of a chaotic system from partial observations. Under reasonable …

We study a continuous data assimilation algorithm proposed by Azouani, Olson, and Titi
(AOT) in the context of an unknown viscosity. We determine the large-time error between the …

We study a nonlinear-nudging modification of the Azouani–Olson–Titi continuous data
assimilation (downscaling) algorithm for the 2D incompressible Navier–Stokes equations …

In this paper, the convergence of an algorithm for recovering the unknown kinematic
viscosity of a two-dimensional incompressible, viscous fluid is studied. The algorithm of …

Understanding the impact of model error on data assimilation is an important practical topic.
Model error in the subgrid scale is commonly seen in various applications as a natural …

We develop an algorithm based on the nudging data assimilation scheme for the concurrent
(on-the-fly) estimation of scalar parameters for a system of evolutionary dissipative partial …

Large eddy simulations of turbulent flows are powerful tools used in many engineering and
geophysical settings. Choosing the right value of the free parameters for their subgrid scale …

We study the use of the Azouani-Olson-Titi (AOT) continuous data assimilation algorithm to
recover solutions of the 3D Navier–Stokes equations modified to have finer-order fractional …