Predicting the critical behavior of complex dynamic systems via learning the governing mechanisms

Xiangrong Wang, Dan Lu, Zongze Wu, Weina Xu, Hongru Hou, Yanqing Hu and Yamir Moreno

Chaos, Solitons & Fractals, 2025, vol. 198, issue C

Abstract: Critical points separate distinct dynamical regimes of complex systems, often delimiting functional or macroscopic phases in which the system operates. However, the long-term prediction of critical regimes and behaviors is challenging given the narrow set of parameters from which they emerge. Here, we propose a framework to learn the rules that govern the dynamic processes of a system. The learned governing rules further refine and guide the representative learning of neural networks from a series of dynamic graphs. This combination enables knowledge-based prediction for the critical behaviors of dynamical networked systems. We evaluate the performance of our framework in predicting two typical critical behaviors in spreading dynamics on various synthetic and real-world networks. Our results show that governing rules can be learned effectively and significantly improve prediction accuracy. Our framework demonstrates a scenario for facilitating the representability of deep neural networks through learning the underlying mechanism, which aims to steer applications for predicting complex behavior that learnable physical rules can drive.

Keywords: Critical behavior of complex dynamic systems; Spreading dynamics; Graph neural networks (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:198:y:2025:i:c:s0960077925005284

DOI: 10.1016/j.chaos.2025.116515

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