2 papers
hep-ex2026
An interpretable unsupervised representation learning for high precision measurement in particle physics
Xing-Jian Lv, De-Xing Miao, Zi-Jun Xu +1
Unsupervised learning has been widely applied to various tasks in particle physics. However, existing models lack precise control over their learned representations, limiting physi…
nlin.CD2025
Adapting Physics-Informed Neural Networks for Bifurcation Detection in Ecological Migration Models
Lujie Yin, Xing Lv
In this study, we explore the application of Physics-Informed Neural Networks (PINNs) to the analysis of bifurcation phenomena in ecological migration models. By integrating the fu…