68 citations · 69 across the 6 of their papers we have counts for
6 papers
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…
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…
Efficient pseudometrics for data-driven comparisons of nonlinear dynamical systems
Bryan Glaz
Computationally efficient solutions for pseudometrics quantifying deviation from topological conjugacy between dynamical systems are presented. Deviation from conjugacy is quantifi…
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…
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…