79 citations · 183 across the 12 of their papers we have counts for
12 papers
Basin Geometry and Reliable Recall of Dynamical Memories in Reservoir Computing
Ling-Wei Kong, Ying-Cheng Lai
Reliable attractor recall conventionally requires broad basins of attraction. However, in reservoir-computing based associative memory, temporal cues reliably recover dynamical mem…
A Continuous-Time and State-Space Relaxation of the Linear Threshold Model with Nonlinear Opinion Dynamics
Ian Xul Belaustegui, Himani Sinhmar, Ling-Wei Kong +2
The Linear Threshold Model (LTM) is widely used to study the propagation of collective behaviors as complex contagions. However, its dependence on discrete states and timesteps res…
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…
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…
Model-free tracking control of complex dynamical trajectories with machine learning
Zheng-Meng Zhai, Mohammadamin Moradi, Ling-Wei Kong +3
Nonlinear tracking control enabling a dynamical system to track a desired trajectory is fundamental to robotics, serving a wide range of civil and defense applications. In control…
Emergence of a stochastic resonance in machine learning
Zheng-Meng Zhai, Ling-Wei Kong, Ying-Cheng Lai
Can noise be beneficial to machine-learning prediction of chaotic systems? Utilizing reservoir computers as a paradigm, we find that injecting noise to the training data can induce…
Digital twins of nonlinear dynamical systems
Ling-Wei Kong, Yang Weng, Bryan Glaz +2
We articulate the design imperatives for machine-learning based digital twins for nonlinear dynamical systems subject to external driving, which can be used to monitor the ``health…
Anticipating synchronization with machine learning
Huawei Fan, Ling-Wei Kong, Ying-Cheng Lai +1
In applications of dynamical systems, situations can arise where it is desired to predict the onset of synchronization as it can lead to characteristic and significant changes in t…
Machine learning prediction of critical transition and system collapse
Ling-Wei Kong, Hua-Wei Fan, Celso Grebogi +1
To predict a critical transition due to parameter drift without relying on model is an outstanding problem in nonlinear dynamics and applied fields. A closely related problem is to…
Synchronization within synchronization: transients and intermittency in ecological networks
Huawei Fan, Ling-Wei Kong, Xingang Wang +2
Transients are fundamental to ecological systems with significant implications to management, conservation, and biological control. We uncover a type of transient synchronization b…
Scaling law of transient lifetime of chimera states under dimension-augmenting perturbations
Ling-Wei Kong, Ying-Cheng Lai
Chimera states arising in the classic Kuramoto system of two-dimensional phase coupled oscillators are transient but they are "long" transients in the sense that the average transi…
Percolation on networks with weak and heterogeneous dependency
Ling-Wei Kong, Ming Li, Run-Ran Liu +1
In real networks, the dependency between nodes is ubiquitous; however, the dependency is not always complete and homogeneous. In this paper, we propose a percolation model with wea…