Speakers
Description
This study proposes a novel framework for clustering dynamical systems to identify parameter combinations that elicit comparable responses of dependent variables under specified initial conditions. Using self-organizing map (SOM) clustering, trajectories are categorized into distinct behavioral regimes determined by parameter variations. A systematic sampling of parameter spaces produces well-defined clusters, revealing underlying patterns associated with excitability and oscillatory dynamics. The method enhances interpretability of complex datasets and supports the detection of critical system modifications. Analytical outputs, including parameter distribution maps, bar charts, and two-dimensional visualizations, are employed to characterize the clusters. As a case study, the FitzHugh–Nagumo model is analyzed, where clustering partitioned the dataset into twelve groups of parameter configurations exhibiting analogous dynamical responses, thereby uncovering fundamental patterns in system behavior.
