10 citations · 38 across the 14 of their papers we have counts for
5 papers · 1 filter
Ensemble-localized Kernel Density Estimation with Applications to the Ensemble Gaussian Mixture Filter
Andrey A. Popov, Enrico M. Zucchelli, Renato Zanetti
The ensemble Gaussian mixture filter (EnGMF) is a non-linear filter suited to data assimilation of highly non-Gaussian and non-linear models that has practical utility in the case…
An Adaptive Covariance Parameterization Technique for the Ensemble Gaussian Mixture Filter
Andrey A Popov, Renato Zanetti
The ensemble Gaussian mixture filter combines the simplicity and power of Gaussian mixture models with the provable convergence and power of particle filters. The quality of the en…
Ensemble Variational Fokker-Planck Methods for Data Assimilation
Amit N Subrahmanya, Andrey A Popov, Adrian Sandu
Particle flow filters solve Bayesian inference problems by smoothly transforming a set of particles into samples from the posterior distribution. Particles move in state space unde…
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,…
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…