Optimal experimental design for infinite-dimensional Bayesian inverse problems governed by PDEs: a review
We present a review of methods for optimal experimental design (OED) for Bayesian inverse problems governed by partial differential equations with infinite-dimensional parameters. The focus is on problems where one seeks to optimize the placement of measurement points, at which data are collected, s...
Saved in:
| Published in | Inverse problems Vol. 37; no. 4; p. 43001 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
01.04.2021
|
| Online Access | Get full text |
| ISSN | 0266-5611 1361-6420 |
| DOI | 10.1088/1361-6420/abe10c |
Cover
| Summary: | We present a review of methods for optimal experimental design (OED) for Bayesian inverse problems governed by partial differential equations with infinite-dimensional parameters. The focus is on problems where one seeks to optimize the placement of measurement points, at which data are collected, such that the uncertainty in the estimated parameters is minimized. We present the mathematical foundations of OED in this context and survey the computational methods for the class of OED problems under study. We also outline some directions for future research in this area. |
|---|---|
| ISSN: | 0266-5611 1361-6420 |
| DOI: | 10.1088/1361-6420/abe10c |