7 papers
The Level Set Ensemble Kalman Filter: Sequential Data Assimilation for Flows With Shocks
Michael K. Sleeman, Lorenzo Beronilla, Hangchuan Hu +4
The ensemble Kalman filter is commonly used to perform sequential data assimilation in science and engineering because of its robust performance and scalability when the state spac…
Large-time behavior and accuracy of the Mean-Field Ensemble Kalman Filter in the Linear Detectable Setting
Franca Hoffmann, Sangmin Park, Andrew M. Stuart
The ensemble Kalman filter (EnKF), originally developed in the geophysical sciences, is now widely used for state and parameter estimation problems in various domains of applicatio…
Neural ensemble Kalman filter: Data assimilation for compressible flows with shocks
Xu-Hui Zhou, Lorenzo Beronilla, Michael K. Sleeman +4
Data assimilation (DA) for compressible flows with shocks is challenging because many classical DA methods generate spurious oscillations and nonphysical features near uncertain sh…
Statistical accuracy of the ensemble Kalman filter in the near-linear setting
E. Calvello, J. A. Carrillo, F. Hoffmann +3
Estimating the state of a dynamical system from partial and noisy observations is a ubiquitous problem in a large number of applications, such as probabilistic weather forecasting…
Accuracy of the Ensemble Kalman Filter in the Near-Linear Setting
Edoardo Calvello, Pierre Monmarché, Andrew M. Stuart +1
The filtering distribution captures the statistics of the state of a dynamical system from partial and noisy observations. Classical particle filters provably approximate this dist…
Statistical Accuracy of Approximate Filtering Methods
J. A. Carrillo, F. Hoffmann, A. M. Stuart +1
Estimating the statistics of the state of a dynamical system, from partial and noisy observations, is both mathematically challenging and finds wide application. Furthermore, the a…
The Mean Field Ensemble Kalman Filter: Near-Gaussian Setting
J. A. Carrillo, F. Hoffmann, A. M. Stuart +1
The ensemble Kalman filter is widely used in applications because, for high dimensional filtering problems, it has a robustness that is not shared for example by the particle filte…