Search Results for author: Bin Shan

Found 6 papers, 2 papers with code

Efficient Discrete Physics-informed Neural Networks for Addressing Evolutionary Partial Differential Equations

no code implementations22 Dec 2023 Siqi Chen, Bin Shan, Ye Li

Physics-informed neural networks (PINNs) have shown promising potential for solving partial differential equations (PDEs) using deep learning.

Transfer Learning

Physics-guided Data Augmentation for Learning the Solution Operator of Linear Differential Equations

no code implementations8 Dec 2022 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.

Data Augmentation Translation

VI-PINNs: Variance-involved Physics-informed Neural Networks for Fast and Accurate Prediction of Partial Differential Equations

1 code implementation30 Nov 2022 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 accurate results, taking less training time, and quantifying the uncertainty of the predicted results.

ERNIE-UniX2: A Unified Cross-lingual Cross-modal Framework for Understanding and Generation

no code implementations9 Nov 2022 Bin Shan, Yaqian Han, Weichong Yin, Shuohuan Wang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang

Recent cross-lingual cross-modal works attempt to extend Vision-Language Pre-training (VLP) models to non-English inputs and achieve impressive performance.

Contrastive Learning Decoder +5

ERNIE-ViL 2.0: Multi-view Contrastive Learning for Image-Text Pre-training

1 code implementation30 Sep 2022 Bin Shan, Weichong Yin, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang

They attempt to learn cross-modal representation using contrastive learning on image-text pairs, however, the built inter-modal correlations only rely on a single view for each modality.

Computational Efficiency Contrastive Learning +7

Cannot find the paper you are looking for? You can Submit a new open access paper.