BANDERA MORENO, ALEJANDRO, Fernandez-Garcia, S. , Gomez-Marmol, M. , Vidal, A.
Si
Commun. Nonlinear Sci. Numer. Simul.
Article
Científica
01/04/2024
001166316100001
In this work, we introduce a novel reduced order model technique, based on the Proper Orthogonal Decomposition method, for dynamical systems with multiple timescales. The main ideas are to retain the structure of the original model, which is lost in the original POD procedure, while producing a competitive reduction in the number of equations and computational time, and to determine the best structure for the reduced system automatically, via a data -driven analysis of the original model data. For these novel techniques, we present some numerical tests for various behaviors of three different neural network models with multiple timescales, which support the use of these new methods.
Slow-fast dynamics; Coupled oscillators; Synchronization; Network neuron model; Reduced order models; Mixed-mode oscillations