Data-Driven Forward Discretizations for Bayesian Inversion
© 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. W...
| Main Authors: | , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2021
|
| Online Access: | https://hdl.handle.net/1721.1/134038 |
| _version_ | 1826210678144237568 |
|---|---|
| author | Bigoni, D Chen, Y Trillos, N Garcia Marzouk, Y Sanz-Alonso, D |
| author2 | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics |
| author_facet | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Bigoni, D Chen, Y Trillos, N Garcia Marzouk, Y Sanz-Alonso, D |
| author_sort | Bigoni, D |
| collection | MIT |
| description | © 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. |
| first_indexed | 2024-09-23T14:53:45Z |
| format | Article |
| id | mit-1721.1/134038 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:53:45Z |
| publishDate | 2021 |
| publisher | IOP Publishing |
| record_format | dspace |
| spelling | mit-1721.1/1340382023-10-05T20:20:48Z Data-Driven Forward Discretizations for Bayesian Inversion Bigoni, D Chen, Y Trillos, N Garcia Marzouk, Y Sanz-Alonso, D Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Statistics and Data Science Center (Massachusetts Institute of Technology) © 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. 2021-10-27T19:57:44Z 2021-10-27T19:57:44Z 2020 2021-05-03T15:51:52Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/134038 en 10.1088/1361-6420/abb2fa Inverse Problems Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf IOP Publishing arXiv |
| spellingShingle | Bigoni, D Chen, Y Trillos, N Garcia Marzouk, Y Sanz-Alonso, D Data-Driven Forward Discretizations for Bayesian Inversion |
| title | Data-Driven Forward Discretizations for Bayesian Inversion |
| title_full | Data-Driven Forward Discretizations for Bayesian Inversion |
| title_fullStr | Data-Driven Forward Discretizations for Bayesian Inversion |
| title_full_unstemmed | Data-Driven Forward Discretizations for Bayesian Inversion |
| title_short | Data-Driven Forward Discretizations for Bayesian Inversion |
| title_sort | data driven forward discretizations for bayesian inversion |
| url | https://hdl.handle.net/1721.1/134038 |
| work_keys_str_mv | AT bigonid datadrivenforwarddiscretizationsforbayesianinversion AT cheny datadrivenforwarddiscretizationsforbayesianinversion AT trillosngarcia datadrivenforwarddiscretizationsforbayesianinversion AT marzouky datadrivenforwarddiscretizationsforbayesianinversion AT sanzalonsod datadrivenforwarddiscretizationsforbayesianinversion |