63 citations · 308 across the 48 of their papers we have counts for
5 papers · 1 filter
A data scalable augmented Lagrangian KKT preconditioner for large scale inverse problems
Nick Alger, Umberto Villa, Tan Bui-Thanh +1
Current state of the art preconditioners for the reduced Hessian and the Karush-Kuhn-Tucker (KKT) operator for large scale inverse problems are typically based on approximating the…
iHDG: An Iterative HDG Framework for Partial Differential Equations
Sriramkrishnan Muralikrishnan, Minh-Binh Tran, Tan Bui-Thanh
We present a scalable iterative solver for high-order hybridized discontinuous Galerkin (HDG) discretizations of linear partial differential equations. It is an interplay between d…
A Data-Scalable Randomized Misfit Approach for Solving Large-Scale PDE-Constrained Inverse Problems
Ellen B. Le, Aaron Myers, Tan Bui-Thanh +1
A randomized misfit approach is presented for the efficient solution of large-scale PDE-constrained inverse problems with high-dimensional data. The purpose of this paper is to off…
A randomized maximum a posterior method for posterior sampling of high dimensional nonlinear Bayesian inverse problems
Kainan Wang, Tan Bui-Thanh, Omar Ghattas
We present a randomized maximum a posteriori (rMAP) method for generating approximate samples of posteriors in high dimensional Bayesian inverse problems governed by large-scale fo…
eHDG:An Exponentially Convergent Iterative Solver for HDG Discretizations of Hyperbolic Partial Differential Equations
Sriramkrishnan Muralikrishnan, Minh-Binh Tran, Tan Bui-Thanh
We present a scalable and efficient iterative solver for high-order hybridized discontinuous Galerkin (HDG) discretizations of hyperbolic partial differential equations. It is an i…