Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data-driven prediction and analysis of chaotic origami dynamics   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.

23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
16
The categories of this paper are: stat
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph