An exploration of data-driven techniques for predicting extreme events in intermittent dynamical systems
Author(s)Guth, Stephen Carrol.
Massachusetts Institute of Technology. Department of Mechanical Engineering.
Themistoklis P. Sapsis.
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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.
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019Cataloged from PDF version of thesis.Includes bibliographical references (pages 111-115).
DepartmentMassachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology