Alen Alexanderian

Orcid: 0000-0002-6371-6618

According to our database1, Alen Alexanderian authored at least 30 papers between 2009 and 2024.

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Bibliography

2024
Robust optimal design of large-scale Bayesian nonlinear inverse problems.
CoRR, 2024

2023
A new perspective on parameter study of optimization problems.
Appl. Math. Lett., June, 2023

Sensitivity Analysis of the Information Gain in Infinite-Dimensional Bayesian Linear Inverse Problems.
CoRR, 2023

2022
Optimal design of large-scale nonlinear Bayesian inverse problems under model uncertainty.
CoRR, 2022

Hyper-differential sensitivity analysis for nonlinear Bayesian inverse problems.
CoRR, 2022

Extreme learning machines for variance-based global sensitivity analysis.
CoRR, 2022

2021
Multiscale Global Sensitivity Analysis for Stochastic Chemical Systems.
Multiscale Model. Simul., 2021

Optimal Design of Large-scale Bayesian Linear Inverse Problems Under Reducible Model Uncertainty: Good to Know What You Don't Know.
SIAM/ASA J. Uncertain. Quantification, 2021

Global sensitivity analysis of rare event probabilities.
CoRR, 2021

2020
Randomization and Reweighted ℓ<sub>1</sub>-Minimization for A-Optimal Design of Linear Inverse Problems.
SIAM J. Sci. Comput., 2020

Variance-based sensitivity analysis for time-dependent processes.
Reliab. Eng. Syst. Saf., 2020

A Distributed Active Subspace Method for Scalable Surrogate Modeling of Function Valued Outputs.
J. Sci. Comput., 2020

Structure exploiting methods for fast uncertainty quantification in multiphase flow through heterogeneous media.
CoRR, 2020

Monte Carlo Estimators for the Schatten p-norm of Symmetric Positive Semidefinite Matrices.
CoRR, 2020

2019
Derivative-Based Global Sensitivity Analysis for Models with High-Dimensional Inputs and Functional Outputs.
SIAM J. Sci. Comput., 2019

Efficient Marginalization-Based MCMC Methods for Hierarchical Bayesian Inverse Problems.
SIAM/ASA J. Uncertain. Quantification, 2019

Sensitivity-Driven Adaptive Construction of Reduced-space Surrogates.
J. Sci. Comput., 2019

Optimal experimental design under irreducible uncertainty for inverse problems governed by PDEs.
CoRR, 2019

Randomization and reweighted 𝓁<sup>1</sup>-minimization for A-optimal design of linear inverse problems.
CoRR, 2019

2018
Efficient D-Optimal Design of Experiments for Infinite-Dimensional Bayesian Linear Inverse Problems.
SIAM J. Sci. Comput., 2018

Goal-Oriented Optimal Design of Experiments for Large-Scale Bayesian Linear Inverse Problems.
CoRR, 2018

2017
Efficient Computation of Sobol' Indices for Stochastic Models.
SIAM J. Sci. Comput., 2017

Randomized matrix-free trace and log-determinant estimators.
Numerische Mathematik, 2017

Mean-Variance Risk-Averse Optimal Control of Systems Governed by PDEs with Random Parameter Fields Using Quadratic Approximations.
SIAM/ASA J. Uncertain. Quantification, 2017

Stability of Nonlinear Convection-Diffusion-Reaction Systems in Discontinuous Galerkin Methods.
J. Sci. Comput., 2017

2016
A Fast and Scalable Method for A-Optimal Design of Experiments for Infinite-dimensional Bayesian Nonlinear Inverse Problems.
SIAM J. Sci. Comput., 2016

2014
A-Optimal Design of Experiments for Infinite-Dimensional Bayesian Linear Inverse Problems with Regularized ℓ<sub>0</sub>-Sparsification.
SIAM J. Sci. Comput., 2014

Preconditioned Bayesian Regression for Stochastic Chemical Kinetics.
J. Sci. Comput., 2014

2012
Multiscale Stochastic Preconditioners in Non-intrusive Spectral Projection.
J. Sci. Comput., 2012

2009
JHelioviewer: Visualizing Large Sets of Solar Images Using JPEG 2000.
Comput. Sci. Eng., 2009


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