8 papers
Can Transformers predict system collapse in dynamical systems?
Zheng-Meng Zhai, Celso Grebogi, Ying-Cheng Lai
Transformer architectures have recently surged as promising solutions for nonlinear dynamical systems, proposed as foundation models capable of zero-shot dynamics reconstruction an…
Anticipating tipping in spatiotemporal systems with machine learning
Smita Deb, Zheng-Meng Zhai, Mulugeta Haile +1
In nonlinear dynamical systems, tipping refers to a critical transition from one steady state to another, typically catastrophic, steady state, often resulting from a saddle-node b…
Deficiency of equation-finding approach to data-driven modeling of dynamical systems
Zheng-Meng Zhai, Valerio Lucarini, Ying-Cheng Lai
Finding the governing equations from data by sparse optimization has become a popular approach to deterministic modeling of dynamical systems. Considering the physical situations w…
Optimizing disorder with machine learning to harness synchronization
Jun-Yin Huang, Zheng-Meng Zhai, Vassilios Kovanis +1
Disorder is often considered detrimental to coherence. However, under specific conditions, it can enhance synchronization. We develop a machine-learning framework to design optimal…
Unsupervised learning for anticipating critical transitions
Shirin Panahi, Ling-Wei Kong, Bryan Glaz +2
For anticipating critical transitions in complex dynamical systems, the recent approach of parameter-driven reservoir computing requires explicit knowledge of the bifurcation param…
Reconstructing dynamics from sparse observations with no training on target system
Zheng-Meng Zhai, Jun-Yin Huang, Benjamin D. Stern +1
In applications, an anticipated situation is where the system of interest has never been encountered before and sparse observations can be made only once. Can the dynamics be faith…