2026-09-19T01:24:24Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1042922022-01-13T07:54:05Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Michael J. Demkowicz and Youssef M. Marzouk.
Aggarwal, Raghav
Massachusetts Institute of Technology. Department of Mechanical Engineering.
Massachusetts Institute of Technology. Department of Mechanical Engineering
2016-09-13T19:22:13Z
2016-09-13T19:22:13Z
2016
2016
http://hdl.handle.net/1721.1/104292
958162630
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 49-52).
In this thesis, we implement Bayesian inference and Bayesian experiment design in a model materials science problem. We demonstrate that by observing the behavior of a film deposited on a substrate, certain features of the substrate may be inferred, with quantified uncertainty. We show that Bayesian experimental design can be used to design efficient experiments. The substrate in this model problem is a Gaussian random field, and the film is a phase separating mixture modeled by the Cahn-Hilliard equation. A key feature of the inference and the experiment design is a stochastic reduced-order model.
by Raghav Aggarwal.
S.M.
52 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Mechanical Engineering.
Demonstration of Bayesian inference and Bayesian experimental design in a model film/substrate inference problem
Thesis
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