68 citations · 79 across the 11 of their papers we have counts for
12 papers
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…
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…
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…