Reconstruction of dynamical systems from data without time labels
Machine Learning
2025-02-26 v3 Numerical Analysis
Dynamical Systems
Numerical Analysis
Abstract
In this paper, we study the method to reconstruct dynamical systems from data without time labels. Data without time labels appear in many applications, such as molecular dynamics, single-cell RNA sequencing etc. Reconstruction of dynamical system from time sequence data has been studied extensively. However, these methods do not apply if time labels are unknown. Without time labels, sequence data becomes distribution data. Based on this observation, we propose to treat the data as samples from a probability distribution and try to reconstruct the underlying dynamical system by minimizing the distribution loss, sliced Wasserstein distance more specifically. Extensive experiment results demonstrate the effectiveness of the proposed method.
Cite
@article{arxiv.2312.04038,
title = {Reconstruction of dynamical systems from data without time labels},
author = {Zhijun Zeng and Pipi Hu and Chenglong Bao and Yi Zhu and Zuoqiang Shi},
journal= {arXiv preprint arXiv:2312.04038},
year = {2025}
}