Search Results for author:
Found 4 papers, 2 papers with code
Preserving linear invariants in ensemble filtering methods
Finally, we assess the benefits of preserving linear invariants for the ensemble Kalman filter and nonlinear ensemble filters.
Leveraging joint sparsity in hierarchical Bayesian learning
We present a hierarchical Bayesian learning approach to infer jointly sparse parameter vectors from multiple measurement vectors.
Sequential image recovery using joint hierarchical Bayesian learning
We specifically consider the case where each data set is missing vital information, which prevents the accurate recovery of the individual images.
Stabilizing Radial Basis Function Methods for Conservation Laws Using Weakly Enforced Boundary Conditions
It is well understood that boundary conditions (BCs) may cause global radial basis function (RBF) methods to become unstable for hyperbolic conservation laws (CLs).
Numerical Analysis Numerical Analysis 35L65, 41A05, 41A30, , 65D05, 65M12
