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01/11/2023
On Bayes risk of the posterior mean in linear inverse problems
We recall some basics regarding the concept of Bayes risk in the context...
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12/22/2022
A brief note on Bayesian D-optimality criterion
We consider finite-dimensional Bayesian linear inverse problems with Gau...
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11/08/2022
Optimal design of large-scale nonlinear Bayesian inverse problems under model uncertainty
We consider optimal experimental design (OED) for Bayesian nonlinear inv...
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09/23/2022
A new perspective on parameter study of optimization problems
We provide a new perspective on the study of parameterized optimization ...
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02/04/2022
Hyper-differential sensitivity analysis for nonlinear Bayesian inverse problems
We consider hyper-differential sensitivity analysis (HDSA) of nonlinear ...
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01/14/2022
Extreme learning machines for variance-based global sensitivity analysis
Variance-based global sensitivity analysis (GSA) can provide a wealth of...
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10/26/2021
Global sensitivity analysis of rare event probabilities
By their very nature, rare event probabilities are expensive to compute;...
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08/22/2020
Structure exploiting methods for fast uncertainty quantification in multiphase flow through heterogeneous media
We present a computational framework for dimension reduction and surroga...
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06/21/2020
Optimal design of large-scale Bayesian linear inverse problems under reducible model uncertainty: good to know what you don't know
We consider optimal design of infinite-dimensional Bayesian linear inver...
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05/26/2020
Global sensitivity analysis informed model reduction and selection applied to a Valsalva maneuver model
In this study, we develop a methodology for model reduction and selectio...
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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...
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03/17/2020
Multiscale global sensitivity analysis for stochastic chemical systems
Sensitivity analysis is routinely performed on simplified surrogate mode...
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12/18/2019
Optimal experimental design under irreducible uncertainty for inverse problems governed by PDEs
We present a method for computing A-optimal sensor placements for infini...
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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...
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02/12/2019
Derivative-based global sensitivity analysis for models with high-dimensional inputs and functional outputs
We present a framework for derivative-based global sensitivity analysis ...
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11/02/2018
Efficient Marginalization-based MCMC Methods for Hierarchical Bayesian Inverse Problems
Hierarchical models in Bayesian inverse problems are characterized by an...
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06/16/2018
Sensitivity-driven adaptive construction of reduced-space surrogates
We develop a systematic approach for surrogate model construction in red...
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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 (...
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11/21/2017