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nlin.CD2025
On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions
Jake Buzhardt, C. Ricardo Constante-Amores, Michael D. Graham
This work explores the relationship between state space methods and Koopman operator-based methods for predicting the time-evolution of nonlinear dynamical systems. We demonstrate…
nlin.CD2024
Data-driven prediction of large-scale spatiotemporal chaos with distributed low-dimensional models
C. Ricardo Constante-Amores, Alec J. Linot, Michael D. Graham
Complex chaotic dynamics, seen in natural and industrial systems like turbulent flows and weather patterns, often span vast spatial domains with interactions across scales. Accurat…