Revealing hidden dynamics from time-series data by ODENet

Pipi Hu Yau Mathematical Sciences Center, Tsinghua University, Beijing, 100084, China Wuyue Yang Yau Mathematical Sciences Center, Tsinghua University, Beijing, 100084, China Yi Zhu Yau Mathematical Sciences Center, Tsinghua University, Beijing, 100084, China Liu Hong School of Mathematics, Sun Yat-Sen University, Guangzhou, 510275, China

Dynamical Systems mathscidoc:2204.11001

2020.10
To derive the hidden dynamics from observed data is one of the fundamental but also challenging problems in many different fields. In this study, we propose a new type of interpretable network called the ordinary differential equation network (ODENet), in which the numerical integration of explicit ordinary differential equations (ODEs) are embedded into the machine learning scheme to build a general framework for revealing the hidden dynamics buried in massive time-series data efficiently and reliably. ODENet takes full advantage of both machine learning algorithms and ODE modeling. On one hand, the embedding of ODEs makes the framework more interpretable benefiting from the mature theories of ODEs. On the other hand, the schemes of machine learning enable data handling, paralleling, and optimization to be easily and efficiently implemented. From classical Lotka-Volterra equations to chaotic Lorenz equations, the ODENet exhibits its remarkable capability in handling time-series data even in the presence of large noise. We further apply the ODENet to real actin aggregation data, which shows an impressive performance as well. These results demonstrate the superiority of ODENet in dealing with noisy data, data with either non-equal spacing or large sampling time steps over other traditional machine learning algorithms.
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@inproceedings{pipi2020revealing,
  title={Revealing hidden dynamics from time-series data by ODENet},
  author={Pipi Hu, Wuyue Yang, Yi Zhu, and Liu Hong},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20220422140342800332096},
  year={2020},
}
Pipi Hu, Wuyue Yang, Yi Zhu, and Liu Hong. Revealing hidden dynamics from time-series data by ODENet. 2020. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20220422140342800332096.
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