activity
20172024
most citedData-Driven Filtered Reduced Order Modeling Of Fluid Flows

8 citations · 8 across the 6 of their papers we have counts for

collaborators

6 papers

math.NA2024

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

Amanda Diegel, Xuejian Li, Leo G. Rebholz

This paper considers continuous data assimilation (CDA) in partial differential equation (PDE) discretizations where nudging parameters can be taken arbitrarily large. We prove tha…

math.NA2024

Analysis of the Picard-Newton iteration for the Navier-Stokes equations: global stability and quadratic convergence

Sara Pollock, Leo Rebholz, Xuemin Tu +1

We analyze and test a simple-to-implement two-step iteration for the incompressible Navier-Stokes equations that consists of first applying the Picard iteration and then applying t…

math.NA2024

Enhancing nonlinear solvers for the Navier-Stokes equations with continuous (noisy) data assimilation

Bosco Garcia-Archilla, Xuejian Li, Julia Novo +1

We consider nonlinear solvers for the incompressible, steady (or at a fixed time step for unsteady) Navier-Stokes equations in the setting where partial measurement data of the sol…

math.AP2023

Accelerating and enabling convergence of nonlinear solvers for Navier-Stokes equations by continuous data assimilation

Xuejian Li, Elizabeth V. Hawkins, Leo G. Rebholz +1

This paper considers improving the Picard and Newton iterative solvers for the Navier-Stokes equations in the setting where data measurements or solution observations are available…

math.NA2023

Removing splitting/modeling error in projection/penalty methods for Navier-Stokes simulations with continuous data assimilation

Elizabeth Hawkins, Leo G. Rebholz, Duygu Vargun

We study continuous data assimilation (CDA) applied to projection and penalty methods for the Navier-Stokes (NS) equations. Penalty and projection methods are more efficient than c…

physics.flu-dyn20178 cited

Data-Driven Filtered Reduced Order Modeling Of Fluid Flows

X. Xie, M. Mohebujjaman, L. G. Rebholz +1

We propose a data-driven filtered reduced order model (DDF-ROM) framework for the numerical simulation of fluid flows. The novel DDF-ROM framework consists of two steps: (i) In the…