Demonstration of Bayesian inference and Bayesian experimental design in a model film/substrate inference problem

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2016

Author dc:creator

• Aggarwal, Raghav

Advisor dc:contributor.advisor

• Michael J. Demkowicz and Youssef M. Marzouk.

• Mechanical Engineering.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/104292

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/104292