Dr. Xu’s research focuses on numerical methods for partial differential equations and big data, specifically finite element methods, multigrid methods and deep neural networks for their theoretical analysis, algorithmic development and practical applications.

Recently, he has devoted himself to mathematical studies of deep learning, working on topics such as the approximation theory of deep neural networks. He has also been developing convolutional neural networks and training algorithms from the multigrid viewpoint and subspace corrections method.