21 citations · 44 across the 4 of their papers we have counts for
6 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…
Adaptive and accuracy-aware multiple data assimilation in a three step framework
Kyle Ivey, Matthias Morzfeld, Chaoyi Wang +4
The ensemble smoother with multiple data assimilation (ES-MDA) is an algorithmic framework for the ensemble-based solution of inverse problems in reservoir engineering (and beyond)…
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…
The Ensemble Kalman Inversion Race
Rebecca Gjini, Matthias Morzfeld, Oliver R. A. Dunbar +1
Ensemble Kalman methods were initially developed to solve nonlinear data assimilation problems in oceanography but are now popular in applications far beyond their original use cas…
Localization in Ensemble Kalman inversion
Xin T. Tong, Matthias Morzfeld
Ensemble Kalman inversion (EKI) is a technique for the numerical solution of inverse problems. A great advantage of the EKI's ensemble approach is that derivatives are not required…
Parameter estimation by implicit sampling
Matthias Morzfeld, Xuemin Tu, Jon Wilkening +1
Implicit sampling is a weighted sampling method that is used in data assimilation, where one sequentially updates estimates of the state of a stochastic model based on a stream of…