An exploration of data-driven techniques for predicting extreme events in intermittent dynamical systems
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2020
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| Online Access: | https://hdl.handle.net/1721.1/123755 |
| _version_ | 1811090265080856576 |
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| author | Guth, Stephen Carrol. |
| author2 | Themistoklis P. Sapsis. |
| author_facet | Themistoklis P. Sapsis. Guth, Stephen Carrol. |
| author_sort | Guth, Stephen Carrol. |
| collection | MIT |
| description | Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019 |
| first_indexed | 2024-09-23T14:40:17Z |
| format | Thesis |
| id | mit-1721.1/123755 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T14:40:17Z |
| publishDate | 2020 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1237552020-02-11T03:09:18Z An exploration of data-driven techniques for predicting extreme events in intermittent dynamical systems Guth, Stephen Carrol. Themistoklis P. Sapsis. Massachusetts Institute of Technology. Department of Mechanical Engineering. Massachusetts Institute of Technology. Department of Mechanical Engineering Mechanical Engineering. 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 2020-02-10T21:41:52Z 2020-02-10T21:41:52Z 2019 2019 Thesis https://hdl.handle.net/1721.1/123755 1138950159 eng 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 115 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Mechanical Engineering. Guth, Stephen Carrol. An exploration of data-driven techniques for predicting extreme events in intermittent dynamical systems |
| title | An exploration of data-driven techniques for predicting extreme events in intermittent dynamical systems |
| title_full | An exploration of data-driven techniques for predicting extreme events in intermittent dynamical systems |
| title_fullStr | An exploration of data-driven techniques for predicting extreme events in intermittent dynamical systems |
| title_full_unstemmed | An exploration of data-driven techniques for predicting extreme events in intermittent dynamical systems |
| title_short | An exploration of data-driven techniques for predicting extreme events in intermittent dynamical systems |
| title_sort | exploration of data driven techniques for predicting extreme events in intermittent dynamical systems |
| topic | Mechanical Engineering. |
| url | https://hdl.handle.net/1721.1/123755 |
| work_keys_str_mv | AT guthstephencarrol anexplorationofdatadriventechniquesforpredictingextremeeventsinintermittentdynamicalsystems AT guthstephencarrol explorationofdatadriventechniquesforpredictingextremeeventsinintermittentdynamicalsystems |