Data-Driven Forward Discretizations for Bayesian Inversion
Name
2003.07991.pdf
Description
Submitted version
Size
3.05 MB
Format
Adobe PDF
Checksum (MD5)
ef0b0ec201bb9582ea6ed22d8d7b509b
Author(s)
Bigoni, D
Chen, Y
Trillos, N Garcia
Marzouk, Y
Sanz-Alonso, D
Date Issued
2020
Journal
Inverse Problems
Publisher
IOP Publishing
Version
Original manuscript
Abstract
© 2020 IOP Publishing Ltd. This paper suggests a framework for the learning of discretizations of expensive forward models in Bayesian inverse problems. The main idea is to incorporate the parameters governing the discretization as part of the unknown to be estimated within the Bayesian machinery. We numerically show that in a variety of inverse problems arising in mechanical engineering, signal processing and the geosciences, the observations contain useful information to guide the choice of discretization.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Statistics and Data Science Center (Massachusetts Institute of Technology)
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1088/1361-6420/abb2fa
