87 citations · 133 across the 11 of their papers we have counts for
4 papers · 1 filter
Discovery of partial differential equations from highly noisy and sparse data with physics-informed information criterion
Hao Xu, Junsheng Zeng, Dongxiao Zhang
Data-driven discovery of PDEs has made tremendous progress recently, and many canonical PDEs have been discovered successfully for proof-of-concept. However, determining the most p…
Predicting the Stereoselectivity of Chemical Transformations by Machine Learning
Justin Li, Dakang Zhang, Yifei Wang +3
Stereoselective reactions (both chemical and enzymatic reactions) have been essential for origin of life, evolution, human biology and medicine. Since late 1960s, there have been n…
Robust discovery of partial differential equations in complex situations
Hao Xu, Dongxiao Zhang
Data-driven discovery of partial differential equations (PDEs) has achieved considerable development in recent years. Several aspects of problems have been resolved by sparse regre…
Topological Regularization for Graph Neural Networks Augmentation
Rui Song, Fausto Giunchiglia, Ke Zhao +1
The complexity and non-Euclidean structure of graph data hinder the development of data augmentation methods similar to those in computer vision. In this paper, we propose a featur…