Computational Advancements for Solving Large-scale Inverse Problems

Computational Advancements for Solving Large-scale Inverse Problems

For many scientific applications, inverse problems have played a key role in solving important problems by enabling researchers to estimate desired parameters of a system from observed measurements. For example, large-scale inverse problems arise in many global problems and medical imaging problems...

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Bibliographic Details
Main Author: Cho, Taewon
Other Authors: Mathematics
Format: Others
Published: Virginia Tech 2021
Subjects:
inverse problems
uncertainty quantification
generalized Golub–Kahan
hybrid projection methods
Tikhonov regularization
Bayesian inverse problems
sample covariance matrix
blind deconvolution
alternating optimization
tomography
respirometry
Online Access:http://hdl.handle.net/10919/103772

Internet

http://hdl.handle.net/10919/103772

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