Physics (Internal)

Predicting Catastrophes: The Role of Criticality

This event is part of the Departmental Seminars.

William Klein, Harvey Gould, Plamen Ivanov, Alex Sushkov and Chris Grant.

Abstract:

Is prediction feasible in systems at criticality? While conventional scale-invariant argument suggests a negative answer, evidences 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 seminar, I will address the question of predictability at criticality by investigating a non-equilibrium system, a driven-dissipative system called the OFC model which is used to describe earthquake faults, and a non-equilibrium process, damage spreading in the Ising model. Both systems display a phase transition at the critical point. 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. This implies that large earthquakes (catastrophes) off the Gutenberg-Richter scaling are distinguishable from the smaller earthquakes. I also show that as the critical point is approached, predictability falls. For damage spreading in the Ising model, an 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 precursors to the 'catastrophic' events.