A Tailored Physics-informed Neural Network Method for Solving Singularly Perturbed Differential Equations. - Yiwen Pang; Ye Li 037; Sheng-Jun Huang (ACAI, 2022)

Implicit Stochastic Gradient Descent for Training Physics-Informed Neural Networks. - Ye Li 037; Songcan Chen; Sheng-Jun Huang (AAAI, 2023)

Causality-enhanced Discreted Physics-informed Neural Networks for Predicting Evolutionary Equations. - Ye Li 037; Siqi Chen; Bin Shan; Sheng-Jun Huang (IJCAI, 2024)

Improving Generalization of Deep Neural Networks by Optimum Shifting. - Yuyan Zhou; Ye Li 037; Lei Feng 006; Sheng-Jun Huang (AAAI, 2025)

VI-PINNs: Variance-involved physics-informed neural networks for fast and accurate prediction of partial differential equations. - Bin Shan; Ye Li 037; Sheng-Jun Huang (Neurocomputing, 2025)