Name
Title | ||
|---|---|---|
A Bayesian inference approach to identify a Robin coefficient in one-dimensional parabolic problems |
Abstract | ||
|---|---|---|
This paper investigates a nonlinear inverse problem associated with the heat conduction problem of identifying a Robin coefficient from boundary temperature measurement. A Bayesian inference approach is presented for the solution of this problem. The prior modeling is achieved via the Markov random field (MRF). The use of a hierarchical Bayesian method for automatic selection of the regularization parameter in the function estimation inverse problem is discussed. The Markov chain Monte Carlo (MCMC) algorithm is used to explore the posterior state space. Numerical results indicate that MRF provides an effective prior regularization, and the Bayesian inference approach can provide accurate estimates as well as uncertainty quantification to the solution of the inverse problem. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.cam.2009.05.007 | J. Computational Applied Mathematics |
Keywords | Field | DocType |
one-dimensional parabolic problem,markov random field,prior modeling,function estimation inverse problem,heat conduction problem,nonlinear inverse problem,inverse problem,markov chain,robin coefficient,effective prior regularization,hierarchical bayesian method,bayesian inference approach,uncertainty quantification,bayesian inference,heat transfer,heat conduction,state space,bayesian method,temperature measurement,markov chain monte carlo | Mathematical optimization,Bayesian experimental design,Bayesian inference,Markov chain Monte Carlo,Markov random field,Markov chain,Bayesian linear regression,Inverse problem,Bayesian statistics,Mathematics | Journal |
Volume | Issue | ISSN |
231 | 2 | 0377-0427 |
Citations | PageRank | References |
3 | 0.53 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liang Yan | 1 | 13 | 3.55 |
| Fenglian Yang | 2 | 27 | 3.83 |
| Chu-Li Fu | 3 | 142 | 28.78 |