This paper expands the analysis of randomized low-rank approximation bey...
This paper is interested in developing reduced order models (ROMs) for
r...
Reconstructing high-resolution flow fields from sparse measurements is a...
We develop hybrid projection methods for computing solutions to large-sc...
This paper tackles efficient methods for Bayesian inverse problems with
...
We advocate a numerically reliable and accurate approach for practical
p...
There are several challenges associated with inverse problems in which w...
The Tensor-Train (TT) format is a highly compact low-rank representation...
In this paper, we introduce a method for multivariate function approxima...
We consider efficient methods for computing solutions to dynamic inverse...
We provide a computational framework for approximating a class of struct...
This paper considers the approximation of a monomial x^n over the interv...
Analyzing massive spatial datasets using Gaussian process model poses
co...
We present numerical methods for computing the Schatten p-norm of positi...
Eigensystem Realization Algorithm (ERA) is a data-driven approach for
su...
The generalized singular value decomposition (GSVD) is a valuable tool t...
We consider optimal design of PDE-based Bayesian linear inverse problems...
Many applications in data science and scientific computing involve
large...
Hierarchical models in Bayesian inverse problems are characterized by an...
We develop a framework for goal oriented optimal design of experiments
(...
We consider the problem of performing matrix completion with side inform...