4 papers · 1 filter
Lyapunov Stable Graph Neural Flow
Haoyu Chu, Xiaotong Chen, Wei Zhou +4
Graph Neural Networks (GNNs) are highly vulnerable to adversarial perturbations in both topology and features, making the learning of robust representations a critical challenge. I…
Efficient Training of Neural Fractional-Order Differential Equation via Adjoint Backpropagation
Qiyu Kang, Xuhao Li, Kai Zhao +4
Fractional-order differential equations (FDEs) enhance traditional differential equations by extending the order of differential operators from integers to real numbers, offering g…
Neural Variable-Order Fractional Differential Equation Networks
Wenjun Cui, Qiyu Kang, Xuhao Li +4
Neural differential equation models have garnered significant attention in recent years for their effectiveness in machine learning applications.Among these, fractional differentia…
Structure-Preserving Physics-Informed Neural Networks With Energy or Lyapunov Structure
Haoyu Chu, Yuto Miyatake, Wenjun Cui +2
Recently, there has been growing interest in using physics-informed neural networks (PINNs) to solve differential equations. However, the preservation of structure, such as energy…