  This research addresses the challenge of characterizing the complexity and
unpredictability of basins within various dynamical systems. The main focus is
on demonstrating the efficiency of convolutional neural networks (CNNs) in this
field. Conventional methods become computationally demanding when analyzing
multiple basins of attraction across different parameters of dynamical systems.
Our research presents an innovative approach that employs CNN architectures for
this purpose, showcasing their superior performance in comparison to
conventional methods. We conduct a comparative analysis of various CNN models,
highlighting the effectiveness of our proposed characterization method while
acknowledging the validity of prior approaches. The findings not only showcase
the potential of CNNs but also emphasize their significance in advancing the
exploration of diverse behaviors within dynamical systems.
