3 citations · 3 across the 3 of their papers we have counts for
5 papers
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…
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,…
A Multifidelity Ensemble Kalman Filter with Reduced Order Control Variates
Andrey A Popov, Changhong Mou, Traian Iliescu +1
This work develops a new multifidelity ensemble Kalman filter (MFEnKF) algorithm based on linear control variate framework. The approach allows for rigorous multifidelity extension…
An Explicit Probabilistic Derivation of Inflation in a Scalar Ensemble Kalman Filter for Finite Step, Finite Ensemble Convergence
Andrey A Popov, Adrian Sandu
This paper uses a probabilistic approach to analyze the converge of an ensemble Kalman filter solution to an exact Kalman filter solution in the simplest possible setting, the scal…