dc.contributor.author |
Nicholson, Ruanui |
en |
dc.contributor.author |
Petra, N |
en |
dc.contributor.author |
Kaipio, Jari |
en |
dc.date.accessioned |
2020-06-12T00:48:42Z |
en |
dc.date.issued |
2018 |
en |
dc.identifier.issn |
0266-5611 |
en |
dc.identifier.uri |
http://hdl.handle.net/2292/51513 |
en |
dc.description.abstract |
We consider the reconstruction of a heterogeneous coefficient field in a Robin boundary condition on an inaccessible part of the boundary in a Poisson problem with an uncertain (or unknown) inhomogeneous conductivity field in the interior of the domain. To account for model errors that stem from the uncertainty in the conductivity coefficient, we treat the unknown conductivity as a nuisance parameter and carry out approximative premarginalization over it, and invert for the Robin coefficient field only. We approximate the related modelling errors via the Bayesian approximation error (BAE) approach. The uncertainty analysis presented here relies on a local linearization of the parameter-to-observable map at the maximum a posteriori (MAP) estimates, which leads to a normal (Gaussian) approximation of the parameter posterior density. To compute the MAP point we apply an inexact Newton conjugate gradient approach based on the adjoint methodology. The construction of the covariance is made tractable by invoking a low-rank approximation of the data misfit component of the Hessian. Two numerical experiments are considered: one where the prior covariance on the conductivity is isotropic, and one where the prior covariance on the conductivity is anisotropic. Results are compared to those based on standard error models, with particular emphasis on the feasibility of the posterior uncertainty estimates. We show that the BAE approach is a feasible one in the sense that the predicted posterior uncertainty is consistent with the actual estimation errors, while neglecting the related modelling error yields infeasible estimates for the Robin coefficient. In addition, we demonstrate that the BAE approach is approximately as computationally expensive (measured in the number of PDE solves) as the conventional error approach. |
en |
dc.language |
English |
en |
dc.publisher |
IOP Publishing |
en |
dc.relation.ispartofseries |
Inverse Problems |
en |
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
en |
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
en |
dc.subject |
Science & Technology |
en |
dc.subject |
Physical Sciences |
en |
dc.subject |
Mathematics, Applied |
en |
dc.subject |
Physics, Mathematical |
en |
dc.subject |
Mathematics |
en |
dc.subject |
Physics |
en |
dc.subject |
estimation of Robin coefficient |
en |
dc.subject |
Modelling errors |
en |
dc.subject |
adjoint-based Hessian |
en |
dc.subject |
low rank approximation |
en |
dc.subject |
Bayesian approximation error approach |
en |
dc.subject |
approximate marginalization |
en |
dc.subject |
Bayesian framework |
en |
dc.subject |
INVERSE PROBLEMS |
en |
dc.subject |
MODEL-REDUCTION |
en |
dc.subject |
NEWTON METHOD |
en |
dc.subject |
PARAMETERS |
en |
dc.subject |
ALGORITHM |
en |
dc.subject |
CORROSION |
en |
dc.subject |
EQUATION |
en |
dc.title |
Estimation of the Robin coefficient field in a Poisson problem with uncertain conductivity field |
en |
dc.type |
Journal Article |
en |
dc.identifier.doi |
10.1088/1361-6420/aad91e |
en |
pubs.issue |
11 |
en |
pubs.volume |
34 |
en |
dc.rights.holder |
Copyright: The author |
en |
pubs.author-url |
https://iopscience.iop.org/article/10.1088/1361-6420/aad91e |
en |
pubs.publication-status |
Published |
en |
dc.rights.accessrights |
http://purl.org/eprint/accessRights/RestrictedAccess |
en |
pubs.subtype |
Article |
en |
pubs.elements-id |
721722 |
en |
pubs.org-id |
Engineering |
en |
pubs.org-id |
Engineering Science |
en |
pubs.org-id |
Science |
en |
pubs.org-id |
Mathematics |
en |
pubs.arxiv-id |
1801.03592 |
en |
dc.identifier.eissn |
1361-6420 |
en |
pubs.number |
115005 |
en |
pubs.record-created-at-source-date |
2020-06-16 |
en |