m (Move page script moved page Draft Content 652313104 to Juarez et al 2021b) |
|||
| (One intermediate revision by one other user not shown) | |||
| Line 10: | Line 10: | ||
== Document == | == Document == | ||
| + | <pdf>Media:Draft_Content_652313104_6637_P_IDC6_437.pdf</pdf> | ||
Latest revision as of 09:41, 1 July 2021
Summary
We consider a statistical inversion computational model with Gaussian distributions for the numerical solution of the Cauchy problem for the Laplace equation. The a priori model is built up from Gaussian Markov random fields. Different precision matrices for the Cauchy problem are introduced. We take advantage of the relationship between the a priori distribution and traditional Tikhonov regularization to propose different models where smooth and non-smooth regularization is possible. A low range analysis allow us to estimate the optimal dimension of data and its relation to the the unknown.
Video
Abstract
Document
Document information
Published on 01/07/21
Accepted on 01/07/21
Submitted on 01/07/21
Volume CT10 - Optimization and Inverse Problems, 2021
DOI: 10.23967/admos.2021.064
Licence: CC BY-NC-SA license
Share this document
Keywords
claim authorship
Are you one of the authors of this document?