10 citations · 18 across the 5 of their papers we have counts for
5 papers
Efficient Discrete Physics-informed Neural Networks for Addressing Evolutionary Partial Differential Equations
Siqi Chen, Bin Shan, Ye Li
Physics-informed neural networks (PINNs) have shown promising potential for solving partial differential equations (PDEs) using deep learning. However, PINNs face training difficul…
Physics-guided Data Augmentation for Learning the Solution Operator of Linear Differential Equations
Ye Li, Yiwen Pang, Bin Shan
Neural networks, especially the recent proposed neural operator models, are increasingly being used to find the solution operator of differential equations. Compared to traditional…
VI-PINNs: Variance-involved Physics-informed Neural Networks for Fast and Accurate Prediction of Partial Differential Equations
Bin Shan, Ye Li, Shengjun Huang
Although physics-informed neural networks(PINNs) have progressed a lot in many real applications recently, there remains problems to be further studied, such as achieving more accu…
ERNIE-UniX2: A Unified Cross-lingual Cross-modal Framework for Understanding and Generation
Bin Shan, Yaqian Han, Weichong Yin +5
Recent cross-lingual cross-modal works attempt to extend Vision-Language Pre-training (VLP) models to non-English inputs and achieve impressive performance. However, these models f…
ERNIE-ViL 2.0: Multi-view Contrastive Learning for Image-Text Pre-training
Bin Shan, Weichong Yin, Yu Sun +3
Recent Vision-Language Pre-trained (VLP) models based on dual encoder have attracted extensive attention from academia and industry due to their superior performance on various cro…