论文标题
数据驱动的预测和混乱折纸动力学的分析
Data-driven prediction and analysis of chaotic origami dynamics
论文作者
论文摘要
机器学习的进步已彻底改变了从自然语言处理到营销再到医疗保健的应用程序的能力。在这里，我们证明了机器学习在预测复杂非线性机械系统中混沌行为方面的功效。具体而言，我们使用准循环神经网络来预测从多息折纸系统获得的极混乱的时间序列数据。此外，尽管机器学习通常被视为“黑匣子”，但在这项研究中，我们进行了隐藏的层分析，以了解神经网络不仅可以准确地处理周期性的数据，而且还可以准确地处理混乱的数据。同样，我们的方法在不依赖折纸系统的数学模型的情况下，在嘈杂的振动环境中表征和预测混乱的动力学方面有效性。因此，我们的方法是完全数据驱动的，并且有可能用于复杂场景，例如薄壁结构的非线性动力学和生物膜系统。
Advances in machine learning have revolutionized capabilities in applications ranging from natural language processing to marketing to health care. Here, we demonstrate the efficacy of machine learning in predicting chaotic behavior in complex nonlinear mechanical systems. Specifically, we use quasi-recurrent neural networks to predict extremely chaotic time series data obtained from multistable origami systems. Additionally, while machine learning is often viewed as a "black box", in this study we conduct hidden layer analysis to understand how the neural network can process not only periodic, but also chaotic data in an accurate manner. Also, our approach shows its effectiveness in characterizing and predicting chaotic dynamics in a noisy environment of vibrations without relying on a mathematical model of origami systems. Therefore, our method is fully data-driven and has the potential to be used for complex scenarios, such as the nonlinear dynamics of thin-walled structures and biological membrane systems.
