8 citations · 17 across the 14 of their papers we have counts for
23 papers
A Two-Level Galerkin Reduced Order Model for the Steady Navier-Stokes Equations
Dylan Park, Changhong Mou, Honghu Liu +2
We propose, analyze, and investigate numerically a novel two-level Galerkin reduced order model (2L-ROM) for the efficient and accurate numerical simulation of the steady Navier-St…
The Model Forest Ensemble Kalman Filter
Andrey A Popov, Adrian Sandu
Traditional data assimilation uses information obtained from the propagation of one physics-driven model and combines it with information derived from real-world observations in or…
Eliminating Order Reduction on Linear, Time-Dependent ODEs with GARK Methods
Steven Roberts, Adrian Sandu
When applied to stiff, linear differential equations with time-dependent forcing, Runge-Kutta methods can exhibit convergence rates lower than predicted by the classical order cond…
Investigation of Nonlinear Model Order Reduction of the Quasigeostrophic Equations through a Physics-Informed Convolutional Autoencoder
Rachel Cooper, Andrey A. Popov, Adrian Sandu
Reduced order modeling (ROM) is a field of techniques that approximates complex physics-based models of real-world processes by inexpensive surrogates that capture important dynami…
Multifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Physics-Informed Autoencoders
Andrey A Popov, Adrian Sandu
Data assimilation is a Bayesian inference process that obtains an enhanced understanding of a physical system of interest by fusing information from an inexact physics-based model,…
Multirate Linearly-Implicit GARK Schemes
Michael Guenther, Adrian Sandu
Many complex applications require the solution of initial-value problems where some components change fast, while others vary slowly. Multirate schemes apply different step sizes t…