2026-09-19T14:27:31Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1237552021-07-05T14:03:20Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Themistoklis P. Sapsis.
Guth, Stephen Carrol.
Massachusetts Institute of Technology. Department of Mechanical Engineering.
Massachusetts Institute of Technology. Department of Mechanical Engineering
2020-02-10T21:41:52Z
2020-02-10T21:41:52Z
2019
2019
https://hdl.handle.net/1721.1/123755
1138950159
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 111-115).
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.
by Stephen Carrol Guth.
S.M.
S.M. Massachusetts Institute of Technology, Department of Mechanical Engineering
115 pages
eng
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Mechanical Engineering.
An exploration of data-driven techniques for predicting extreme events in intermittent dynamical systems
Thesis
application/pdf
2020-02-10T21:41:51Z
Publication
Master
MechE
unknown
unknown
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