Blackbox Stencil Interpolation Method for model reduction

Model reduction often requires modifications to the simulation code. In many circumstances, developing and maintaining these modifications can be cumbersome. Non-intrusive methods that do not require modification to the source code are often preferred. This thesis proposed a new formulation of machine learning, Black-box Stencil Interpolation Method, for this purpose. It is a non-intrusive, data-oriented method to infer the underlying physics that governs a simulation, which can be combined with conventional intrusive model reduction techniques. This method is tested on several problems to investigate its accuracy, robustness, and applicabilities.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2012

Author dc:creator

• Chen, Han, S.M. Massachusetts Institute of Technology

Advisor dc:contributor.advisor

• Qiqi Wang.

• Aeronautics and Astronautics.

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/1721.1/77103

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/77103