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 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 architecture to ensure that the parameter of interest lies within the unit interval, and we refer to the resulting neural model as KANE (KAN with Natural Enforcement). The proposed method is backed by exhaustive numerical studies and further illustrated with real-world applications to seismology and climatology.

翻译：本文针对级联极端事件（例如极端地震后引发海啸）日益增长的关注，提出了一种专注于此类多米诺效应风险评估的新方法。所提出的方法在Kolmogorov-Arnold网络（KAN）中构建了一个极值理论框架，用于在给定特征向量的条件下，估计一个极端事件触发另一个极端事件的概率。我们在KAN架构中增加了一个额外层，以确保目标参数位于单位区间内，并将由此产生的神经模型称为KANE（具有自然约束的KAN）。所提出的方法得到了详尽数值研究的支持，并进一步通过地震学和气候学的实际应用加以阐释。