3 papers
nlin.CD2025
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…
q-bio.QM2024
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…
math.DS2024
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…