Event Details

A Kolmogorov–Arnold Neural Model for Cascading Extremes

This paper addresses the growing concern of cascading extreme events, such as an extreme earthquake followed by a tsunami, by presenting a novel method for risk assessment focused on these domino effects. The proposed approach develops an extreme value theory framework within a Kolmogorov–Arnold Neural Network (KAN) to estimate the probability of one extreme event triggering another, conditionally on a feature vector. An extra layer is added to the KAN’s architecture to enforce the definition of the parameter of interest within the unit interval. The proposed method is backed by exhaustive numerical studies and further illustrated with a real-life application to seismology.

Joint work with C. Ferrer and R. Vallejos.