Optimal Experimental Design for Infinite-dimensional Bayesian Inverse Problems Governed by PDEs: A Review
Optimization and Control
2021-02-01 v3
Abstract
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.
Cite
@article{arxiv.2005.12998,
title = {Optimal Experimental Design for Infinite-dimensional Bayesian Inverse Problems Governed by PDEs: A Review},
author = {Alen Alexanderian},
journal= {arXiv preprint arXiv:2005.12998},
year = {2021}
}
Comments
37 pages; minor revisions; added more references; article accepted for publication in Inverse Problems