000161707 001__ 161707
000161707 005__ 20251017144620.0
000161707 0247_ $$2doi$$a10.1016/j.chaos.2025.116515
000161707 0248_ $$2sideral$$a144338
000161707 037__ $$aART-2025-144338
000161707 041__ $$aeng
000161707 100__ $$aWang, Xiangrong
000161707 245__ $$aPredicting the critical behavior of complex dynamic systems via learning the governing mechanisms
000161707 260__ $$c2025
000161707 5060_ $$aAccess copy available to the general public$$fUnrestricted
000161707 5203_ $$aCritical 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.
000161707 536__ $$9info:eu-repo/grantAgreement/ES/DGA/E36-23R-FENOL$$9info:eu-repo/grantAgreement/ES/MICINN/PID2023-149409NB-I00
000161707 540__ $$9info:eu-repo/semantics/embargoedAccess$$aby-nc-nd$$uhttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
000161707 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000161707 700__ $$aLu, Dan
000161707 700__ $$aWu, Zongze
000161707 700__ $$aXu, Weina
000161707 700__ $$aHou, Hongru
000161707 700__ $$aHu, Yanqing
000161707 700__ $$0(orcid)0000-0002-0895-1893$$aMoreno, Yamir$$uUniversidad de Zaragoza
000161707 7102_ $$12004$$2405$$aUniversidad de Zaragoza$$bDpto. Física Teórica$$cÁrea Física Teórica
000161707 773__ $$g198 (2025), 116515 [8 pp.]$$pChaos, solitons fractals$$tChaos, Solitons and Fractals$$x0960-0779
000161707 8564_ $$s6743948$$uhttps://zaguan.unizar.es/record/161707/files/texto_completo.pdf$$yPostprint$$zinfo:eu-repo/date/embargoEnd/2027-05-28
000161707 8564_ $$s1546749$$uhttps://zaguan.unizar.es/record/161707/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint$$zinfo:eu-repo/date/embargoEnd/2027-05-28
000161707 909CO $$ooai:zaguan.unizar.es:161707$$particulos$$pdriver
000161707 951__ $$a2025-10-17-14:21:24
000161707 980__ $$aARTICLE