Isaac Paul Sunseri

College of Sciences

Works (3)

Updated: July 5th, 2023 14:29

2022 journal article

Hyper-differential sensitivity analysis for inverse problems governed by ODEs with application to COVID-19 modeling

MATHEMATICAL BIOSCIENCES, 351.

By: M. Stevens  n, I. Sunseri n & A. Alexanderian n

author keywords: Inverse problems; Sensitivity analysis; Uncertainty quantification; Design of experiments; Computational epidemiology
MeSH headings : Algorithms; COVID-19; Humans; Linear Models; Models, Biological; Uncertainty
TL;DR: This work proposes a linear approximation to the solution of the inverse problem that allows efficiently approximating the statistical properties of the estimated parameters and explores the use of this linear model for approximate global sensitivity analysis. (via Semantic Scholar)
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Added: September 26, 2022

2021 journal article

Optimal Design of Large-scale Bayesian Linear Inverse Problems Under Reducible Model Uncertainty: Good to Know What You Don't Know

SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION, 9(1), 163–184.

By: A. Alexanderian*, N. Petra, G. Stadler & I. Sunseri*

author keywords: optimal experimental design; Bayesian inference; inverse problems; model uncertainty; sensor placement; sparsified designs
TL;DR: This work derives a marginalized A-optimality criterion and develops an efficient computational approach for its optimization of infinite-dimensional Bayesian linear inverse problems governed by partial differential equations that contain secondary reducible model uncertainties, in addition to the uncertainty in the inversion parameters. (via Semantic Scholar)
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Added: May 10, 2021

2020 journal article

Hyper-differential sensitivity analysis for inverse problems constrained by partial differential equations

INVERSE PROBLEMS, 36(12).

By: I. Sunseri n, J. Hart  n, B. Bloemen Waanders n & A. Alexanderian n

author keywords: inverse problems; sensitivity analysis; design of experiments; subsurface flow; model uncertainty
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Added: December 21, 2020

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