A Kolmogorov-Arnold Neural Model for Cascading Extremes
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 37 references that are in the map. First posted on arXiv 19 May 2025.
Builds on
7 of 37 references.
References · 7
- 2024KAN: Kolmogorov-Arnold Networkscited by 649
- 2019Error bounds for deep ReLU networks using the Kolmogorov--Arnold superposition theoremcited by 49
- 2022ExSpliNet: An interpretable and expressive spline-based neural networkcited by 33
- 2020ExGAN: Adversarial Generation of Extreme Samplescited by 29
- 2023On Regression in Extreme Regionscited by 6
- 2023Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theorycited by 5
- 2023Ordinal classification for interval-valued data and interval-valued functional datacited by 1
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Citing papers per year: 2026: 1.
1 paper.