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DTSTAMP:20261018T170721Z
LAST-MODIFIED:20190924T144009Z
DTSTART:20190925T200000Z
DTEND:20190925T210000Z
UID:event2185@bu.edu
URL:http://physics.bu.edu/internal/events/show/2185
SUMMARY:Predicting Catastrophes: The Role of Criticality
DESCRIPTION:Featuring Chon Kit Pun (Jack)\n\nPart of the Departmental Semin
	ars.\n\nWilliam Klein\, Harvey Gould\, Plamen Ivanov\, Alex Sushkov and Chr
	is Grant.\n\nAbstract:\n\nIs prediction feasible in systems at criticality?
	 While conventional scale-invariant argument suggests a negative answer\, e
	vidences from simulation of driven-dissipative systems and real systems suc
	h as ruptures in material and crashes in the financial market have suggeste
	d otherwise. \n\nIn this seminar\, I will address the question of predictab
	ility at criticality by investigating a non-equilibrium system\, a driven-d
	issipative system called the OFC model which is used to describe earthquake
	 faults\, and a non-equilibrium process\, damage spreading in the Ising mod
	el. Both systems display a phase transition at the critical point. Using ma
	chine learning\, I show that in the OFC model\, scaling events are indistin
	guishable from one another and only the large\, non-scaling events are dist
	inguishable from the small\, scaling events. This implies that large earthq
	uakes (catastrophes) off the Gutenberg-Richter scaling are distinguishable 
	from the smaller earthquakes. I also show that as the critical point is app
	roached\, predictability falls. For damage spreading in the Ising model\, a
	n opposite behavior is seen: the accuracy of predicting whether damage will
	 spread indefinitely (catastrophe) or heal increases as the critical point 
	is approached. I will also use machine learning to identify useful precurso
	rs to the 'catastrophic' events.
LOCATION:SCI 352\, 590 Commonwealth Avenue\, 02215
STATUS:CONFIRMED
CLASS:PUBLIC
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