Demonstration of Bayesian inference and Bayesian experimental design in a model film/substrate inference problem
Name
958162630-MIT.pdf
Description
Full printable version
Size
3.13 MB
Format
Adobe PDF
Checksum (MD5)
45d6d3ec5bfc3e7ad139e3946fee8c9f
Author(s)
Aggarwal, Raghav
Advisor(s)
Michael J. Demkowicz and Youssef M. Marzouk.
Date Issued
2016
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 49-52).
Subjects
Mechanical Engineering.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
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