dc.contributor.advisor | Themistoklis P. Sapsis. | en_US |
dc.contributor.author | Guth, Stephen Carrol. | en_US |
dc.contributor.other | Massachusetts Institute of Technology. Department of Mechanical Engineering. | en_US |
dc.date.accessioned | 2020-02-10T21:41:52Z | |
dc.date.available | 2020-02-10T21:41:52Z | |
dc.date.copyright | 2019 | en_US |
dc.date.issued | 2019 | en_US |
dc.identifier.uri | https://hdl.handle.net/1721.1/123755 | |
dc.description | Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019 | en_US |
dc.description | Cataloged from PDF version of thesis. | en_US |
dc.description | Includes bibliographical references (pages 111-115). | en_US |
dc.description.abstract | The ability to characterize and predict extreme events is a vital topic in fields ranging from finance to ocean engineering. Typically, the most-extreme events are also the most-rare, and it is this property that makes data collection and direct simulation challenging. In this thesis, I will develop a data-driven objective, alpha-star, appropriate for optimizing extreme event predictor schemes. This objective is constructed from the same principles as Reciever Operating Characteristic Curves, and exhibits a geometric connection to scale separation. Additionally, I will demonstrate the application of alpha-star to the advance prediction of intermittent extreme events in the Majda-McLaughlin-Tabak model of a dispersive fluid. | en_US |
dc.description.statementofresponsibility | by Stephen Carrol Guth. | en_US |
dc.format.extent | 115 pages | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Massachusetts Institute of Technology | en_US |
dc.rights | MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. | en_US |
dc.rights.uri | http://dspace.mit.edu/handle/1721.1/7582 | en_US |
dc.subject | Mechanical Engineering. | en_US |
dc.title | An exploration of data-driven techniques for predicting extreme events in intermittent dynamical systems | en_US |
dc.type | Thesis | en_US |
dc.description.degree | S.M. | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Mechanical Engineering | en_US |
dc.identifier.oclc | 1138950159 | en_US |
dc.description.collection | S.M. Massachusetts Institute of Technology, Department of Mechanical Engineering | en_US |
dspace.imported | 2020-02-10T21:41:51Z | en_US |
mit.thesis.degree | Master | en_US |
mit.thesis.department | MechE | en_US |