This study showcases the effectiveness of convolutional neural networks (CNNs) in characterizing the complexity and unpredictability of basins of attraction for diverse dynamical systems. This novel method is optimal for exploring different parameters of dynamical systems since the conventional methods are computationally expensive for characterizing multiple basins of attraction. Additionally, our research includes a comparison of different CNN architectures for this task showing the superiority of our proposed characterization method over the conventional methods, even with obsolete architectures.

翻译：本研究展示了卷积神经网络（CNN）在表征不同动力系统吸引域复杂性与不可预测性方面的有效性。由于传统方法在表征多个吸引域时计算成本高昂，该新方法对于探索动力系统的不同参数具有最优性。此外，本研究还比较了不同CNN架构在该任务中的表现，结果表明即便采用过时架构，我们所提出的表征方法仍优于传统方法。