An exploration of data-driven techniques for predicting extreme events in intermittent dynamical systems
Name
1138950159-MIT.pdf
Size
9.76 MB
Format
Adobe PDF
Checksum (MD5)
ee4f8f03e9f66f1d31cbb177abd6e039
Author(s)
Guth, Stephen Carrol.
Advisor(s)
Themistoklis P. Sapsis.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
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.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 111-115).
Subjects
Mechanical Engineering.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
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