Disorder-aided Early Warning Signals: Predicting Catastrophic Shifts in Athermal Systems
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918225085726720 |
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| author | Bar, Tapas Banerjee, Anurag Casals, Blai Catalan, Gustau Rodríguez-Viejo, Javier |
| author_facet | Bar, Tapas Banerjee, Anurag Casals, Blai Catalan, Gustau Rodríguez-Viejo, Javier |
| contents | The early prediction of tipping points, distinguished by sudden and catastrophic shifts from stable states, poses a challenging task that would enable us to assess the impending threat across natural and engineered systems. This threat becomes particularly acute in low-fluctuation environments, where tipping occurs through saddle-node bifurcation without prior warning in noise dynamics. In this study, we investigate the tipping point dynamics of avalanche catastrophes in low-fluctuation domain, employing model system like the zero temperature random field Ising model and thermally deposited cobalt films. As the system approaches the tipping point, avalanche activity reveals pronounced critical behaviour, including critical slowing down, variance enhancement, and a growing spatial correlation length--hallmarks that may serve as early warning signals of impending collapse. Crucially, we demonstrate that increasing disorder in the system reduces its vulnerability to catastrophic failure. In highly disorder regimes, these early warning signals emerge well before the transition, thereby providing a large margin for anticipation and mitigation. This key finding suggests a protective role of disorder offering a novel perspective on resilience in complex systems. Our results not only deepen the understanding of tipping phenomena in disorder materials but also have broader implications for forecasting regime shift in diverse real-world systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_01601 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Disorder-aided Early Warning Signals: Predicting Catastrophic Shifts in Athermal Systems Bar, Tapas Banerjee, Anurag Casals, Blai Catalan, Gustau Rodríguez-Viejo, Javier Statistical Mechanics The early prediction of tipping points, distinguished by sudden and catastrophic shifts from stable states, poses a challenging task that would enable us to assess the impending threat across natural and engineered systems. This threat becomes particularly acute in low-fluctuation environments, where tipping occurs through saddle-node bifurcation without prior warning in noise dynamics. In this study, we investigate the tipping point dynamics of avalanche catastrophes in low-fluctuation domain, employing model system like the zero temperature random field Ising model and thermally deposited cobalt films. As the system approaches the tipping point, avalanche activity reveals pronounced critical behaviour, including critical slowing down, variance enhancement, and a growing spatial correlation length--hallmarks that may serve as early warning signals of impending collapse. Crucially, we demonstrate that increasing disorder in the system reduces its vulnerability to catastrophic failure. In highly disorder regimes, these early warning signals emerge well before the transition, thereby providing a large margin for anticipation and mitigation. This key finding suggests a protective role of disorder offering a novel perspective on resilience in complex systems. Our results not only deepen the understanding of tipping phenomena in disorder materials but also have broader implications for forecasting regime shift in diverse real-world systems. |
| title | Disorder-aided Early Warning Signals: Predicting Catastrophic Shifts in Athermal Systems |
| topic | Statistical Mechanics |
| url | https://arxiv.org/abs/2509.01601 |