5 citations · 5 across the 8 of their papers we have counts for
5 papers · 1 filter
A new perspective on parameter study of optimization problems
Alen Alexanderian, Joseph Hart, Mason Stevens
We provide a new perspective on the study of parameterized optimization problems. Our approach combines methods for post-optimal sensitivity analysis and ordinary differential equa…
Optimal design of large-scale Bayesian linear inverse problems under reducible model uncertainty: good to know what you don't know
Alen Alexanderian, Noemi Petra, Georg Stadler +1
We consider optimal design of infinite-dimensional Bayesian linear inverse problems governed by partial differential equations that contain secondary reducible model uncertainties,…
Optimal Experimental Design for Infinite-dimensional Bayesian Inverse Problems Governed by PDEs: A Review
Alen Alexanderian
We present a review of methods for optimal experimental design (OED) for Bayesian inverse problems governed by partial differential equations with infinite-dimensional parameters.…
Hyper-Differential Sensitivity Analysis for Inverse Problems Constrained by Partial Differential Equations
Isaac Sunseri, Joseph Hart, Bart van Bloemen Waanders +1
High fidelity models used in many science and engineering applications couple multiple physical states and parameters. Inverse problems arise when a model parameter cannot be deter…
Optimal experimental design under irreducible uncertainty for linear inverse problems governed by PDEs
Karina Koval, Alen Alexanderian, Georg Stadler
We present a method for computing A-optimal sensor placements for infinite-dimensional Bayesian linear inverse problems governed by PDEs with irreducible model uncertainties. Here,…