34 citations · 55 across the 5 of their papers we have counts for
5 papers
Preserving linear invariants in ensemble filtering methods
Mathieu Le Provost, Jan Glaubitz, Youssef Marzouk
Data assimilation combines dynamical models with observations to improve state estimates. Ensemble filters sequentially assimilate observations by updating a set of samples over ti…
An adaptive ensemble filter for heavy-tailed distributions: tuning-free inflation and localization
Mathieu Le Provost, Ricardo Baptista, Jeff D. Eldredge +1
Heavy tails is a common feature of filtering distributions that results from the nonlinear dynamical and observation processes as well as the uncertainty from physical sensors. In…
Bayesian inference of vorticity in unbounded flow from limited pressure measurements
Jeff D. Eldredge, Mathieu Le Provost
We study the instantaneous inference of an unbounded planar flow from sparse noisy pressure measurements. The true flow field comprises one or more regularized point vortices of va…
A low-rank ensemble Kalman filter for elliptic observations
Mathieu Le Provost, Ricardo Baptista, Youssef Marzouk +1
We propose a regularization method for ensemble Kalman filtering (EnKF) with elliptic observation operators. Commonly used EnKF regularization methods suppress state correlations a…
Ensemble-filtered vortex modeling of strongly disturbed aerodynamic flows
Mathieu Le Provost, Jeff D. Eldredge
The task of dynamic flow estimation is to construct an approximation of an evolving flow---and particularly, its response to disturbances---using measurements from available sensor…