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dc.contributor.advisorKlein, Williamen_US
dc.contributor.authorPun, Chon-Kiten_US
dc.date.accessioned2020-03-02T18:21:01Z
dc.date.available2020-03-02T18:21:01Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/2144/39605
dc.description.abstractIs prediction feasible in systems at criticality? While conventional scale-invariant arguments suggest a negative answer, evidence from simulation of driven-dissipative systems and real systems such as ruptures in material and crashes in the financial market have suggested otherwise. In this dissertation, I address the question of predictability at criticality by investigating two non-equilibrium systems: a driven-dissipative system called the OFC model which is used to describe earthquakes and damage spreading in the Ising model. Both systems display a phase transition at the critical point. By using machine learning, I show that in the OFC model, scaling events are indistinguishable from one another and only the large, non-scaling events are distinguishable from the small, scaling events. I also show that as the critical point is approached, predictability falls. For damage spreading in the Ising model, the opposite behavior is seen: the accuracy of predicting whether damage will spread or heal increases as the critical point is approached. I will also use machine learning to understand what are the useful precursors to the prediction problem.en_US
dc.language.isoen_US
dc.subjectPhysicsen_US
dc.titlePredicting catastrophes: the role of criticalityen_US
dc.typeThesis/Dissertationen_US
dc.date.updated2020-02-24T20:02:52Z
etd.degree.nameDoctor of Philosophyen_US
etd.degree.leveldoctoralen_US
etd.degree.disciplinePhysicsen_US
etd.degree.grantorBoston Universityen_US
dc.identifier.orcid0000-0001-5865-7388


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