Advances in machine learning have revolutionized capabilities in applications ranging from natural language processing to marketing to health care. Recently, machine learning techniques have also been employed to learn physics, but one of the formidable challenges is to predict complex dynamics, particularly chaos. Here, we demonstrate the efficacy of quasi-recurrent neural networks in predicting extremely chaotic behavior in multistable origami structures. While machine learning is often viewed as a “black box”, we conduct hidden layer analysis to understand how the neural network can process not only periodic, but also chaotic data in an accurate manner. 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.

机器学习的进步彻底改变了从自然语言处理到市场营销到医疗保健的各种应用程序的功能。最近，机器学习技术也已被用来学习物理学，但是最艰巨的挑战之一是预测复杂的动力学，特别是混乱。在这里，我们证明了准递归神经网络在预测多稳态折纸结构中的极端混沌行为方面的功效。虽然机器学习通常被视为“黑匣子”，但我们进行了隐藏层分析，以了解神经网络如何不仅可以周期性地处理混沌数据，而且还可以准确地处理混沌数据。我们的方法显示了在不依赖折纸系统数学模型的情况下，在表征和预测嘈杂的振动环境中的混沌动力学方面的有效性。因此，