10 citations · 38 across the 14 of their papers we have counts for
15 papers
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…
Small-data Reduced Order Modeling of Chaotic Dynamics through SyCo-AE: Synthetically Constrained Autoencoders
Andrey A. Popov, Renato Zanetti
Data-driven reduced order modeling of chaotic dynamics can result in systems that either dissipate or diverge catastrophically. Leveraging non-linear dimensionality reduction of au…
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…
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…
A Sensor-Based Simulation Method for Spatiotemporal Event Detection
Yuqin Jiang, Andrey A. Popov, Zhenlong Li +2
Human movements in urban areas are essential to understand human-environment interactions. However, activities and associated movements are full of uncertainties due to the complex…
A Meta-learning Formulation of the Autoencoder Problem for Non-linear Dimensionality Reduction
Andrey A. Popov, Arash Sarshar, Austin Chennault +1
A rapidly growing area of research is the use of machine learning approaches such as autoencoders for dimensionality reduction of data and models in scientific applications. We sho…
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…
Adjoint-Matching Neural Network Surrogates for Fast 4D-Var Data Assimilation
Austin Chennault, Andrey A. Popov, Amit N. Subrahmanya +4
Data assimilation is the process of fusing information from imperfect computer simulations with noisy, sparse measurements of reality to obtain improved estimates of the state or p…
A Stochastic Covariance Shrinkage Approach to Particle Rejuvenation in the Ensemble Transform Particle Filter
Andrey A Popov, Amit N Subrahmanya, Adrian Sandu
Rejuvenation in particle filters is necessary to prevent the collapse of the weights when the number of particles is insufficient to sample the high probability regions of the stat…
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 fast time-stepping strategy for dynamical systems equipped with a surrogate model
Steven Roberts, Andrey A Popov, Arash Sarshar +1
Simulation of complex dynamical systems arising in many applications is computationally challenging due to their size and complexity. Model order reduction, machine learning, and o…
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…
A Stochastic Covariance Shrinkage Approach in Ensemble Transform Kalman Filtering
Andrey A Popov, Adrian Sandu, Elias D. Nino-Ruiz +1
The Ensemble Kalman Filters (EnKF) employ a Monte-Carlo approach to represent covariance information, and are affected by sampling errors in operational settings where the number o…