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Qiqi Wang. Chen, Han, S.M. Massachusetts Institute of Technology Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics. Massachusetts Institute of Technology. Department of Aeronautics and Astronautics 2013-02-15T14:38:44Z 2013-02-15T14:38:44Z 2012 2012 http://hdl.handle.net/1721.1/77103 824796573 Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2012. Cataloged from department-submitted PDF version of thesis. This electronic version was submitted and approved by the author's academic department as part of an electronic thesis pilot project. The certified thesis is available in the Institute Archives and Special Collections. Includes bibliographical references (p. 87-89). 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. by Han Chen. S.M. 89 p. 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 Aeronautics and Astronautics. Blackbox Stencil Interpolation Method for model reduction Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="e7b79d73-377e-424f-b9c6-a5089987d39d"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Blackbox Stencil Interpolation Method for model reduction</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2012</PublicationDate> <Authors> <Author> <DisplayName>Chen, Han, S.M. Massachusetts Institute of Technology</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Aeronautics and Astronautics.</Keyword> <Abstract>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.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>