research
∙
08/10/2023
Randomized low-rank approximations beyond Gaussian random matrices
This paper expands the analysis of randomized low-rank approximation bey...
share
research
∙
06/28/2023
Randomized Reduced Basis Methods for Parameterized Fractional Elliptic PDEs
This paper is interested in developing reduced order models (ROMs) for r...
research
∙
08/21/2022
Tensor-based flow reconstruction from optimally located sensor measurements
Reconstructing high-resolution flow fields from sparse measurements is a...
research
∙
06/14/2022
Hybrid Projection Methods for Solution Decomposition in Large-scale Bayesian Inverse Problems
We develop hybrid projection methods for computing solutions to large-sc...
research
∙
05/09/2022
Efficient algorithms for Bayesian Inverse Problems with Whittle–Matérn Priors
This paper tackles efficient methods for Bayesian inverse problems with ...
research
∙
05/09/2022
Robust Parameter Identifiability Analysis via Column Subset Selection
We advocate a numerically reliable and accurate approach for practical p...
research
∙
11/30/2021
Bayesian Level Set Approach for Inverse Problems with Piecewise Constant Reconstructions
There are several challenges associated with inverse problems in which w...
research
∙
10/08/2021
Randomized algorithms for rounding in the Tensor-Train format
The Tensor-Train (TT) format is a highly compact low-rank representation...
research
∙
07/27/2021
Efficient randomized tensor-based algorithms for function approximation and low-rank kernel interactions
In this paper, we introduce a method for multivariate function approxima...
research
∙
07/12/2021
Efficient edge-preserving methods for dynamic inverse problems
We consider efficient methods for computing solutions to dynamic inverse...
research
∙
05/03/2021
Structured Matrix Approximations via Tensor Decompositions
We provide a computational framework for approximating a class of struct...
research
∙
01/18/2021
Approximating monomials using Chebyshev polynomials
This paper considers the approximation of a monomial x^n over the interv...
research
∙
12/24/2020
Kryging: Geostatistical analysis of large-scale datasets using Krylov subspace methods
Analyzing massive spatial datasets using Gaussian process model poses co...
research
∙
05/20/2020
Monte Carlo Estimators for the Schatten p-norm of Symmetric Positive Semidefinite Matrices
We present numerical methods for computing the Schatten p-norm of positi...
research
∙
03/26/2020
Efficient Randomized Algorithms for Subspace System Identification
Eigensystem Realization Algorithm (ERA) is a data-driven approach for su...
research
∙
02/07/2020
Randomized Algorithms for Generalized Singular Value Decomposition with Application to Sensitivity Analysis
The generalized singular value decomposition (GSVD) is a valuable tool t...
research
∙
06/10/2019
Randomization and reweighted ℓ_1-minimization for A-optimal design of linear inverse problems
We consider optimal design of PDE-based Bayesian linear inverse problems...
research
∙
05/17/2019
Randomized algorithms for low-rank tensor decompositions in the Tucker format
Many applications in data science and scientific computing involve large...
research
∙
11/02/2018
Efficient Marginalization-based MCMC Methods for Hierarchical Bayesian Inverse Problems
Hierarchical models in Bayesian inverse problems are characterized by an...
research
∙
02/19/2018
Goal-Oriented Optimal Design of Experiments for Large-Scale Bayesian Linear Inverse Problems
We develop a framework for goal oriented optimal design of experiments (...
research
∙
10/18/2016
