期刊
SCIENCE ADVANCES
卷 7, 期 25, 页码 -出版社
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/sciadv.abf5006
关键词
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资金
- Deutsche Forschungsgemeinschaft (DFG) [SE 2504/2-1]
This study introduces a universal method for data-driven modeling of complex nonlinear dynamics, bridging machine learning, network science, and statistical physics. The proposed cluster-based network modeling (CNM) describes short- and long-term behavior and is fully automatable. This approach complements network connectivity science and offers fast-track avenues for understanding, estimating, predicting, and controlling complex systems in all scientific fields.
We propose a universal method for data-driven modeling of complex nonlinear dynamics from time-resolved snapshot data without prior knowledge. Complex nonlinear dynamics govern many fields of science and engineering. Data-driven dynamic modeling often assumes a low-dimensional subspace or manifold for the state. We liberate ourselves from this assumption by proposing cluster-based network modeling (CNM) bridging machine learning, network science, and statistical physics. CNM describes short- and long-term behavior and is fully automatable, as it does not rely on application-specific knowledge. CNM is demonstrated for the Lorenz attractor, ECG heartbeat signals, Kolmogorov flow, and a high-dimensional actuated turbulent boundary layer. Even the notoriously difficult modeling benchmark of rare events in the Kolmogorov flow is solved. This automatable universal data-driven representation of complex nonlinear dynamics complements and expands network connectivity science and promises new fast-track avenues to understand, estimate, predict, and control complex systems in all scientific fields.
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