Optimal Bayesian inversion in computational uncertainty quantification for PDE models

Karimi, Ahmad

doi:10.34726/hss.2024.100422

Record link:

https://doi.org/10.34726/hss.2024.100422
http://hdl.handle.net/20.500.12708/200055

Title:

Optimal Bayesian inversion in computational uncertainty quantification for PDE models

Citation:

Karimi, A. (2024). Optimal Bayesian inversion in computational uncertainty quantification for PDE models [Dissertation, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2024.100422

reposiTUm DOI:

10.34726/hss.2024.100422

CatalogPlus:

AC17281158

Publication Type:

Thesis - Dissertation

Language:

English

Authors:

Karimi, Ahmad

Advisor:

Heitzinger, Clemens

Organisational Unit:

E101 - Institut für Analysis und Scientific Computing

Date (published):

2024

Number of Pages:

113

Keywords:

Uncertainty Quantification; Bayesian Inversion; Optimal Experimental Design; Expected Information Gain (EIG); PDE Models; Electrical Impedance Tomography (EIT); Computational Science and Engineering

License:

In Copyright

Appears in Collections:

Thesis