8 citations · 9 across the 11 of their papers we have counts for
11 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…
Learning to learn ecosystems from limited data -- a meta-learning approach
Zheng-Meng Zhai, Bryan Glaz, Mulugeta Haile +1
A fundamental challenge in developing data-driven approaches to ecological systems for tasks such as state estimation and prediction is the paucity of the observational or measurem…
Data-driven model discovery with Kolmogorov-Arnold networks
Mohammadamin Moradi, Shirin Panahi, Erik M. Bollt +1
Data-driven model discovery of complex dynamical systems is typically done using sparse optimization, but it has a fundamental limitation: sparsity in that the underlying governing…
Random forests for detecting weak signals and extracting physical information: a case study of magnetic navigation
Mohammadamin Moradi, Zheng-Meng Zhai, Aaron Nielsen +1
It was recently demonstrated that two machine-learning architectures, reservoir computing and time-delayed feed-forward neural networks, can be exploited for detecting the Earth's…
Machine-learning prediction of tipping with applications to the Atlantic Meridional Overturning Circulation
Shirin Panahi, Ling-Wei Kong, Mohammadamin Moradi +4
Anticipating a tipping point, a transition from one stable steady state to another, is a problem of broad relevance due to the ubiquity of the phenomenon in diverse fields. The ste…
Machine-learning parameter tracking with partial state observation
Zheng-Meng Zhai, Mohammadamin Moradi, Bryan Glaz +2
Complex and nonlinear dynamical systems often involve parameters that change with time, accurate tracking of which is essential to tasks such as state estimation, prediction, and c…