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VERSION:2.0
PRODID:-//RLASKEY//CALENDEROUS//EN
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METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20260804T023241Z
LAST-MODIFIED:20191204T150726Z
DTSTART:20191204T163000Z
DTEND:20191204T173000Z
UID:event2240@bu.edu
URL:http://physics.bu.edu/internal/events/show/2240
SUMMARY:Predicting Catastrophes: the Role of Criticality
DESCRIPTION:Featuring Chon Kit Pun (Jack)\n\nPart of the PhD Final Oral Exa
	ms.\n\nDissertation Committee:  William Klein\, Harvey Gould\, Plamen Ivano
	v\, Alex Sushkov\, Christopher Grant\n\nAbstract:\n\nIs prediction feasible
	 in systems at criticality? While conventional scale-invariant arguments su
	ggest a negative answer\, evidence from simulation of driven-dissipative sy
	stems and real systems such as ruptures in material and crashes in the fina
	ncial market have suggested otherwise. \n\nIn this dissertation\, I address
	 the question of predictability at criticality by investigating two non-equ
	ilibrium 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 indistinguish
	able from one another and only the large\, non-scaling events are distingui
	shable from the small\, scaling events. I also show that as the critical po
	int 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 precursor
	s to the prediction problem.
LOCATION:SCI 255\, 590 Commonwealth Avenue\, 02215
STATUS:CONFIRMED
CLASS:PUBLIC
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