2 citations · 6 across the 5 of their papers we have counts for
5 papers
PI-VEGAN: Physics Informed Variational Embedding Generative Adversarial Networks for Stochastic Differential Equations
Ruisong Gao, Yufeng Wang, Min Yang +1
We present a new category of physics-informed neural networks called physics informed variational embedding generative adversarial network (PI-VEGAN), that effectively tackles the…
Less Emphasis on Difficult Layer Regions: Curriculum Learning for Singularly Perturbed Convection-Diffusion-Reaction Problems
Yufeng Wang, Cong Xu, Min Yang +1
Although Physics-Informed Neural Networks (PINNs) have been successfully applied in a wide variety of science and engineering fields, they can fail to accurately predict the underl…
Improve Deep Image Inpainting by Emphasizing the Complexity of Missing Regions
Yufeng Wang, Dan Li, Cong Xu +1
Deep image inpainting research mainly focuses on constructing various neural network architectures or imposing novel optimization objectives. However, on the one hand, building a s…
Missingness Augmentation: A General Approach for Improving Generative Imputation Models
Yufeng Wang, Dan Li, Cong Xu +1
Missing data imputation is a fundamental problem in data analysis, and many studies have been conducted to improve its performance by exploring model structures and learning proced…
PC-GAIN: Pseudo-label Conditional Generative Adversarial Imputation Networks for Incomplete Data
Yufeng Wang, Dan Li, Xiang Li +1
Datasets with missing values are very common in real world applications. GAIN, a recently proposed deep generative model for missing data imputation, has been proved to outperform…