In-context learning to predict critical transitions in dynamical systems
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910213114691584 |
|---|---|
| author | Sevinchan, Yunus Nathaniel, Juan Ueltzhöffer, Kai Roesch, Carla Weber, Tobias Laschos, Vaios Fan, Hang Ramien, Gregor Haux, Johannes Gentine, Pierre Herdeanu, Benjamin |
| author_facet | Sevinchan, Yunus Nathaniel, Juan Ueltzhöffer, Kai Roesch, Carla Weber, Tobias Laschos, Vaios Fan, Hang Ramien, Gregor Haux, Johannes Gentine, Pierre Herdeanu, Benjamin |
| contents | Critical transitions - abrupt, often irreversible changes in system dynamics - arise across human and natural systems, often with catastrophic consequences. Real-world observations of such shifts remain scarce, preventing the development of reliable early warning systems. Conventional statistical and spectral indicators, such as increasing variance, tend to fail under realistic conditions of limited data and correlated noise, whereas existing deep learning classifiers do not extrapolate beyond their training data distribution. In this work, we introduce TipPFN, an in-context learning (ICL) framework that uses a prior-data fitted network to infer a system's proximity to a critical transition. Trained on our novel synthetic data generator, which is based on canonical bifurcation scenarios coupled to diverse, randomized stochastic dynamics, TipPFN flexibly capitalizes on contexts of various sizes, complexity and dimensionalities. We demonstrate robust, state-of-the-art early detection of critical transitions in previously unseen tipping regimes, sim-to-real examples, and real-world observations in both ICL and zero-shot settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_12308 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | In-context learning to predict critical transitions in dynamical systems Sevinchan, Yunus Nathaniel, Juan Ueltzhöffer, Kai Roesch, Carla Weber, Tobias Laschos, Vaios Fan, Hang Ramien, Gregor Haux, Johannes Gentine, Pierre Herdeanu, Benjamin Machine Learning Critical transitions - abrupt, often irreversible changes in system dynamics - arise across human and natural systems, often with catastrophic consequences. Real-world observations of such shifts remain scarce, preventing the development of reliable early warning systems. Conventional statistical and spectral indicators, such as increasing variance, tend to fail under realistic conditions of limited data and correlated noise, whereas existing deep learning classifiers do not extrapolate beyond their training data distribution. In this work, we introduce TipPFN, an in-context learning (ICL) framework that uses a prior-data fitted network to infer a system's proximity to a critical transition. Trained on our novel synthetic data generator, which is based on canonical bifurcation scenarios coupled to diverse, randomized stochastic dynamics, TipPFN flexibly capitalizes on contexts of various sizes, complexity and dimensionalities. We demonstrate robust, state-of-the-art early detection of critical transitions in previously unseen tipping regimes, sim-to-real examples, and real-world observations in both ICL and zero-shot settings. |
| title | In-context learning to predict critical transitions in dynamical systems |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.12308 |